Search

Additive manufacturing (AM) processes are subject to cyber-physical attacks during all the three stages including design, slicing, and manufacturing phases. In-situ process authentication is crucial for AM to ensure that the manufacturing is performed as intended. Since most of the cyber-physical attacks aiming to alter AM processes can be manifested in the change of printing path, an in-situ optical imaging system is capable of detecting alteration in printing path in real time through texture analysis. This will prevent catastrophic geometric changes and mechanical property compromises in the AM parts, and hence improving the AM process security. In this paper, a new part authentication framework is proposed by leveraging layer-wise in-situ videos. The distribution of the segmented textures’ geometric features is extracted from the layer-wise videos, and the multilinear principal component analysis (MPCA) algorithm is used to extract low-dimensional features from the joint distribution of geometric features. A case study based on a fused filament fabrication (FFF) process is used to validate the proposed framework.

Rotatingmachinery is the main component of mechanical equipment. Nevertheless, due to variation of operating condition results in important detection performance deterioration. Therefore, fault detection and diagnosis of rotating machines is very critical for the reliable operation. In this paper, a novel classification technique is employed for fault detection of rotating machines based on kernelled support tensor machine (KSTM) and multilinear principal component analysis (MPCA). The vibration signal is firstly formulated as a 3-way tensor using trial, condition and channel. In order to process the rotating machines faults and identify the information classes in tensor space, the KSTM is then introduced from sets of binary support tensor machine classifiers by the one-against-one parallel strategy. The MPCA is utilized for reduction dimensionality of the high-dimensional signature space and reservation the tensorial structure information. The performance of the developed technique in classification faults of rotating machinery has been thoroughly evaluated through collecting signals on bearing and gear test-rigs. Experimental results showed that the proposed method can achieve the highest classification results among the six classification techniques investigated in this study.

Background Snoring source analysis is essential for an appropriate surgical decision for both simple snorers and obstructive sleep apnea/hypopnea syndrome (OSAHS) patients. Objective As snoring sounds carry significant information about tissue vibrations within the upper airway, a new feature entitled compressed histogram of oriented gradients (CHOG) is proposed to recognize vibration patterns of the snoring source acoustically by compressing histogram of oriented gradients (HOG) descriptors via the multilinear principal component analysis (MPCA) algorithm. Methods Each vibration pattern corresponds to a sole or combinatorial vibration among the four upper airway soft tissues of soft palate, lateral pharyngeal wall, tongue base, and epiglottis. 1037 snoring events from noncontact sound recordings of 76 simple snorers or OSAHS patients during drug-induced sleep endoscopy (DISE) were evaluated. Results With a support vector machine (SVM) as the classifier, the proposed CHOG achieved a recognition accuracy of 89.8% for the seven observable vibration patterns of the snoring source categorized in our most recent work. Conclusion The CHOG outperforms other single features widely used for acoustic analysis of sole vibration site.

During a forging process, the pressure data collected at different positions of the forging machine constitute a group of multi-channel data. Most existing research cannot use these multi-channel d...

Principal component analysis (PCA) is arguably the most widely used dimension-reduction method for vector-type data. When applied to a sample of images, PCA requires vectorization of the image data, which in turn entails solving an eigenvalue problem for the sample covariance matrix. We propose herein a two-stage dimension reduction (2SDR) method for image reconstruction from high-dimensional noisy image data. The first stage treats the image as a matrix, which is a tensor of order 2, and uses multilinear principal component analysis (MPCA) for matrix rank reduction and image denoising. The second stage vectorizes the reduced-rank matrix and achieves further dimension and noise reduction. Simulation studies demonstrate excellent performance of 2SDR, for which we also develop an asymptotic theory that establishes consistency of its rank selection. Applications to cryo-EM (cryogenic electronic microscopy), which has revolutionized structural biology, organic and medical chemistry, cellular and molecular physiology in the past decade, are also provided and illustrated with benchmark cryo-EM datasets. Connections to other contemporaneous developments in image reconstruction and high-dimensional statistical inference are also discussed., Comment: 29 pages, 8 figures and 3 tables

In recent years, radar has been employed as a fall detector because of its effective sensing capabilities and penetration through walls. In this paper, we introduce a multilinear subspace human activity recognition scheme that exploits the three radar signal variables: slow-time, fast-time, and Doppler frequency. The proposed approach attempts to find the optimum subspaces that minimize the reconstruction error for different modes of the radar data cube. A comprehensive analysis of the optimization considerations is performed, such as initialization, number of projections, and convergence of the algorithms. Finally, a boosting scheme is proposed combining the unsupervised multilinear principal component analysis (PCA) with the supervised methods of linear discriminant analysis and shallow neural networks. Experimental results based on real radar data obtained from multiple subjects, different locations, and aspect angles (0 $^{\circ }$ , 30 $^{\circ }$ , 45 $^{\circ }$ , 60 $^{\circ }$ , and 90 $^{\circ }$ ) demonstrate that the proposed algorithm yields the highest overall classification accuracy among spectrogram-based methods including predefined physical features, one- and two-dimensional PCA and convolutional neural networks.

We introduce synchronized and calibrated multi-view video and motion capture dataset for motion analysis and gait identification. The 3D gait dataset consists of 166 data sequences with 32 people. In 128 data sequences, each of 32 individuals was dressed in his/her clothes, in 24 data sequences, 6 of 32 performers changed clothes, and in 14 data sequences, 7 of the performers had a backpack on his/her back. In a single recording session, every performer walked from right to left, then from left to right, and afterwards on the diagonal from upper-right to bottom-left and from bottom-left to upper-right corner of a rectangular scene. We demonstrate that a baseline algorithm achieves promising results in a challenging scenario, in which gallery/training data were collected in walks perpendicular/facing to the cameras, whereas the probe/testing data were collected in diagonal walks. We compare performances of biometric gait recognition that were achieved on marker-less and marker-based 3D data. We present recognition performances, which were achieved by a convolutional neural network and classic classifiers operating on gait signatures obtained by multilinear principal component analysis. The availability of synchronized multi-view image sequences with 3D locations of body markers creates a number of possibilities for extraction of discriminative gait signatures. The gait data are available at http://bytom.pja.edu.pl/projekty/hm-gpjatk/.

Since the volume of the medical data is increasing due to the presence of vast amount of features, the conventional rule mining technique is not competent to handle the data and to perform ...

Recently, tooth decay detection is considered as one of the emerging topics. Many diagnostic techniques have been successfully presented to diagnose the problems. However, the complexity in the tooth decaying diagnosis ascends when the environs are moderately difficult. Thus, this study introduces a novel caries detecting model for the accurate detection of tooth cavities. The model is divided into two phases: feature extraction and classification. Here, the feature extraction is based on multi-linear principal component analysis (MPCA), and the classification is processed using renowned neural network (NN) classifier. The NN classifier is trained using the adaptive dragonfly algorithm (ADA) algorithm. The proposed MPCA model Non-linear Programming with ADA (MNP-ADA) performance is compared with other existing methods and the performance of the approach is analysed in terms of measures such as accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F 1 -score, and Mathews correlation coefficient. The performance of the proposed model is analysed in terms of feature analysis and classifier analysis by comparing other models and proves the superiority of the developed caries detection model.

Due to high dimensionality and multiple variables, unsupervised classification of 12-lead ECG signals involves challenges and difficulties. In order to automatically discover unknown physiological features from raw multivariate signals and detect abnormal cardiac activities of a subject, we proposed an unsupervised classification scheme of 12-lead ECG signals using wavelet tensor decomposition and two-dimensional Gaussian spectral clustering. After filtering and segmentation, each ECG sample is converted into a wavelet tensor by the Discrete Wavelet Packet Transform (DWPT). Main features of ECG samples can be clearly investigated in a multiple feature space constructed by the ECG lead, time and frequency sub-band. Then the Multilinear Principal Component Analysis (MPCA) is applied to reduce the dimensionality of ECG tensors as well as preserve the data interior structure. Taking account of both magnitude and orientation of feature vectors, a novel two-dimensional Gaussian spectral clustering (TGSC) is devised to cluster different 12-lead ECG samples. Furthermore, the dataset obtained from practical 12-lead ECG experiment and two datasets from PhysioBank are used to verify the efficiency of the proposed method. Clustering results show that more useful features of ECG signals can be extracted by the wavelet-tensor-based MPCA than by vector-based PCA. With the two-dimensional Gaussian proximity matrix, the clustering accuracy of TGSC is also higher than that of the traditional spectral clustering.

Half maximal effective concentration EC 50 is considered the main reference for evaluating the efficacy of the products in any plantation using doses and inhibition percentages from laboratory data. However, EC 50 is not the best representation when other relevant variables and their relationships could be involved. As an agricultural case study, fungicide sensitivity of Pseudocercospora fijiensis , the causal agent of black sigatoka, was evaluated on bananas’ plantations in three provinces of Ecuador. In this study, multivariate statistical process control was adjusted to a fungicide efficacy evaluation case considering multiple data tables from different locations and years at the same time. The threshold conveyed by inhibition percentages, related to the EC , along with locations and years allowed the multivariate analyses carried out in the proposal. The multivariate statistical control techniques applied were Multilinear Principal Component Analysis (MPCA) and Dual STATIS-Parallel Coordinates approach (DS-PC). A comparison was developed and showed that both methods discriminate correctly between the normal and anomalous conditions within plantations along years, validating the ability of the novel method DS-PC for exhibiting better signaling of anomalous plantations and performing variable-wise analysis to find out possible causes of this behavior in an easier time-saving graphical framework.

rTensor is an R package designed to provide a common set of operations and decompositions for multidimensional arrays (tensors). We provide an S4 class that wraps around the base 'array' class and overloads familiar operations to users of 'array', and we provide additional functionality for tensor operations that are becoming more relevant in recent literature. We also provide a general unfolding operation, for which the k-mode unfolding and the matrix vectorization are special cases of. Finally, package rTensor implements common tensor decompositions such as canonical polyadic decomposition, Tucker decomposition, multilinear principal component analysis, t-singular value decomposition, as well as related matrix-based algorithms such as generalized low rank approximation of matrices and popular value decomposition.

Additive manufacturing (AM) processes are subject to lower stability compared to their traditional counterparts. The process inconsistency leads to anomalies in the build, which hinders AM’s broader adoption to critical structural component manufacturing. Therefore, it is crucial to detect any process change/anomaly in a timely and accurate manner for potential corrective operations. Real-time thermal image streams captured from AM processes are regarded as most informative signatures of the process stability. Existing state-of-the-art studies on thermal image streams focus merely on in situ sensing, feature extraction, and their relationship with process setup parameters and material properties. The objective of this paper is to develop a statistical process control (SPC) approach to detect process changes as soon as it occurs based on predefined distribution of the monitoring statistics. There are two major challenges: 1) complex spatial interdependence exists in the thermal images and current engineering knowledge is not sufficient to describe all the variability, and 2) the thermal images suffer from a large data volume, a low signal-to-noise ratio, and an ill structure with missing data. To tackle these challenges, multilinear principal component analysis (MPCA) approach is used to extract low dimensional features and residuals. Subsequently, an online dual control charting system is proposed by leveraging multivariate T 2 and Q control charts to detect changes in extracted low dimensional features and residuals, respectively. A real-world case study of thin wall fabrication using a Laser Engineered Net Shaping (LENS) process is used to illustrate the effectiveness of the proposed approach, and the accuracy of process anomaly detection is validated based on X-ray computed tomography information collected from the final build offline.

Many actuators and sensors made by smart materials have hysteresis feature which is a complex nonlinear phenomenon with multivalued mapping. Accurate identification of hysteresis pattern in those sensors and actuators is helpful for improving the modeling and control strategies of the systems. In this paper, a general framework of the pattern clustering of hysteretic time series is developed on the basis of tensor decomposition. First, high-dimensional multivariate data of hysteresis objects are transformed into three-order hysteresis tensors. Then the multilinear principal component analysis (MPCA) is utilized to reduce the dimensionality of hysteresis tensors. Afterward, a novel tensor ${k}$ -means clustering (CTKmeans) based on tensor distance and cycle variation feature initialization is developed for the clustering of tensor objects. In order to evaluate the performance of the proposed approach, the experiment of hysteresis feature test using polyvinylidene fluoride (PVDF)-based pressure sensors is implemented. The measured high-dimensional hysteresis time series of PVDF successively undergoes the procedures of filtering, segmentation, MPCA dimensionality reduction and CTKmeans clustering. The experimental results show that the MPCA can capture more significant inherent features of hysteresis objects than the principal component analysis (PCA) or the kernel PCA in the dimensionality reduction. In terms of tensor distance and cycle variation feature initialization, the CTKmeans outperforms the random-initialization-based tensor ${k}$ -means and standard ${k}$ -means in the clustering of hysteresis tensors. With the tensor decomposition approach of multivariate time series, the hysteresis objects with nonlinear multivalued mapping features can be effectively identified by the combination of MPCA and CTKmeans.

With the increasing number of wearable sensors and mobile devices, human activity recognition (HAR) based on multiple sensors has attracted more and more attention in recent years. On account of the diversity of human actions, the analysis of multivariate signals of activities is still a challenging task. Clustering is an unsupervised classification technique which can directly work on unlabeled data and automatically identify unknown activities. Therefore, a new wavelet tensor fuzzy clustering scheme (WTFCS) for multi-sensor activity recognition is proposed in this paper. Firstly, feature tensors of multiple activity signals are constructed using the discrete wavelet packet transform (DWPT). Then Multilinear Principal Component Analysis (MPCA) is utilized to reduce the dimensionality of feature tensors so as to keep the inherent data structure. On the basis of the principal feature initialization and the tensor fuzzy membership, a new fuzzy clustering (PTFC) is developed to identify different activity feature tensor groups. Finally, the open HAR dataset (DSAD) is used to verify the efficiency of the WTFCS. Clustering results of seventeen activities of eight subjects show that potential useful features of human activities can be captured through combining DWPT-based feature extraction with MPCA-based dimensionality reduction. The PTFC is capable of discriminating various human activities effectively. Its correctness rate of activity recognition is higher than those of fuzzy c-means clustering and the fuzzy clustering based on the tensor distance.

Gear fault diagnosis relies heavily on the scrutiny of vibration responses measured. In reality, gear vibration signals are noisy and dominated by meshing frequencies as well as their harmonics, which oftentimes overlay the fault related components. Moreover, many gear transmission systems, e.g., those in wind turbines, constantly operate under non-stationary conditions. To reduce the influences of non-synchronous components and noise, a fault signature enhancement method that is built upon angle-frequency domain synchronous averaging is developed in this paper. Instead of being averaged in the time domain, the signals are processed in the angle-frequency domain to solve the issue of phase shifts between signal segments due to uncertainties caused by clearances, input disturbances, and sampling errors, etc. The enhanced results are then analyzed through feature extraction algorithms to identify the most distinct features for fault classification and identification. Specifically, Kernel Principal Component Analysis (KPCA) targeting at nonlinearity, Multilinear Principal Component Analysis (MPCA) targeting at high dimensionality, and Locally Linear Embedding (LLE) targeting at local similarity among the enhanced data are employed and compared to yield insights. Numerical and experimental investigations are performed, and the results reveal the effectiveness of angle-frequency domain synchronous averaging in enabling feature extraction and classification.

Recently, the problem of extracting tensor object feature is studied and a very elegant solution, multilinear principal component analysis (MPCA), is proposed, which is motivated as a tool for tensor object dimension reduction and feature extraction by operating directly on the original tensor data. However, the original MPCA is an offline learning method and not suitable for processing online data since it generates the best projection matrices by learning on the whole training data set at once. In this study, we propose an online multilinear principal component analysis (OMPCA) algorithm and prove that the sequence generated by OMPCA converges to a stationary point of the total tensor scatter maximizing problem. Experiment results of an OMPCA-based support higher-order tensor machine for classification, show that OMPCA significantly lowers the time of dimension reduction with little sacrifice of recognition accuracy.

Iris biometric modality possesses inherent characteristics which make the iris recognition system highly reliable and noninvasive. Nowadays, research in this area is challenging compact template size and fast verification algorithms. Special efforts have been employed to minimize the size of the extracted features without degrading the performance of the iris recognition system. In response, we propose an improved feature fusion approach based on multilinear subspace learning to analyze Iris recognition. This approach consists of four stages. In the first stage, the eye image is segmented to extract the iris region. In the second step, wavelet packet decomposition is conducted to extract features of the iris image, since good time and frequency resolutions can be provided simultaneously by the wavelet packet decomposition. In the next step, all decomposed nodes or packets are arranged as a 3rd order tensor rather than a long vector, in which feature fusion is directly implemented with multilinear principal component analysis (MPCA). This approach provides a more compact or useful low-dimensional representation directly from the original tensorial representation. Finally, a discriminative tensor feature selection mechanism and classification strategy are applied to iris recognition problem. The obtained results indicate the usefulness of MPCA to select discriminative features and fuse them effectively. The experimental results reveal that the proposed tensor-based MPCA approach achieved a competitive matching performance on the SDUMLA-HMT Iris database with an adequate acceptable rate.

Dimension reduction methods for matrix or tensor data have been an active research field in recent years. Li et al. (2010) introduced the notion of the Kronecker envelope and proposed dimension folding estimators for supervised dimension reduction. In a data analysis of cryogenic electron microscopy (cryo-EM) images (Chen et al., 2014), Kronecker envelope principal component analysis (PCA) was used to reduce the dimension of cryo-EM images. Kronecker envelope PCA is a two-step procedure, which consists of projecting data onto a multilinear envelope subspace as the first step, followed by ordinary PCA on the projected core tensor. The multilinear envelope subspace preserves the natural Kronecker product structure of observations when searching for the leading principal subspace. The main advantage of preserving the Kronecker product structure is the parsimonious usage of parameters in specifying the leading principal subspace, which mitigates the adverse influence of high-dimensionality. The method of PCA will convert possibly correlated variables to uncorrelated ones and further reduce the dimension of the projected core tensor. In this article we derive the asymptotic normality of Kronecker envelope PCA and compare it with ordinary PCA. Utilizing majorization theory, we show that Kronecker envelope PCA is asymptotically more efficient than ordinary PCA in the sense that the asymptotic total stochastic variation of Kronecker envelope PCA is smaller than that of ordinary PCA. A motivating real data example of cryo-EM image clustering and simulation studies are presented to show the merits of Kronecker envelope PCA.

In this letter, we present to use an information fusion based multilinear analysis approach to classify multi-focal image stacks. First, image fusion techniques such as the nonsubsampled contourlet transform sparse representation (NSCTSR) are used to combine relevant information of multi-focal images within a given image stack into a single image, which is more informative and complete than any single image in the given image stack. Second, multi-focal images within a stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by using canonical correlation analysis (CCA). Finally, because multi-focal image stacks represent the effect of different factors - texture, shape, different instances within the same class and different classes of the objects, we embed the information fusion methods within a multilinear analysis (MA) framework to propose an information fusion based multilinear classifier. The experimental results demonstrated that the information fusion based multilinear classifier can reach a higher classification rate (96.6%) than the previous multilinear based approach (86.4%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work.

The full representation of a d-variate function requires exponentially storage size as a function of dimension d and high computational cost. In order to reduce these complexities, function approximation methods (called reconstruction in our context) are proposed, such as: interpolation, approximation, etc. The traditional interpolation model like the multilinear one, has this dimensional-ity problem. To deal with this problem, we propose a new model based on the Tucker format-a low-rank tensor approximation method, called here the Tucker decomposition. The Tucker decomposition is built as a tensor product of one-dimensional spaces where their one-variate basis functions are constructed by an extension of the KarhunenLo eve decomposition into high-dimensional space. Using this technique, we can acquire, direction by direction, the most important information of the function and convert it into a small number of basis functions. Hence, the approximation for a given function needs less data than that of the multilinear model. Results of a test case on the neutron cross-section reconstruction demonstrate that the Tucker decomposition achieves a better accuracy while using less data than the multilinear interpolation.

In the last few years, great efforts have been made to extend the linear projection technique (LPT) for multidimensional data (i.e., tensor), generally referred to as the multilinear projection technique (MPT). The vectorized nature of LPT requires high-dimensional data to be converted into vector, and hence may lose spatial neighborhood information of raw data. MPT well addresses this problem by encoding multidimensional data as general tensors of a second or even higher order. In this paper, we propose a novel multilinear projection technique, called multilinear spatial discriminant analysis (MSDA), to identify the underlying manifold of high-order tensor data. MSDA considers both the nonlocal structure and the local structure of data in the transform domain, seeking to learn the projection matrices from all directions of tensor data that simultaneously maximize the nonlocal structure and minimize the local structure. Different from multilinear principal component analysis (MPCA) that aims to preserve the global structure and tensor locality preserving projection (TLPP) that is in favor of preserving the local structure, MSDA seeks a tradeoff between the nonlocal (global) and local structures so as to drive its discriminant information from the range of the non-local structure and the range of the local structure. This spatial discriminant characteristic makes MSDA have more powerful manifold preserving ability than TLPP and MPCA. Theoretical analysis shows that traditional MPTs, such as multilinear linear discriminant analysis, TLPP, MPCA, and tensor maximum margin criterion, could be derived from the MSDA model by setting different graphs and constraints. Extensive experiments on face databases (ORL, CMU PIE, and the extended Yale-B) and the Weizmann action database demonstrate the effectiveness of the proposed MSDA method.

Gait recognition is one of the latest and attractive biometric techniques, due to its potential in identification of individuals at a distance, unobtrusively and even using low resolution images. In this paper we focus on single lateral view gait recognition with various carrying and clothing conditions. Such a system is needed in access control applications whereby a single view is imposed by the system setup. The gait data is firstly processed using three gait representation methods as the features sources; Accumulated Prediction Image (API) and two new gait representations namely; Accumulated Flow Image (AFI) and Edge-Masked Active Energy Image (EMAEI). Secondly, each of these methods is tested using three matching classification schemes; image projection with Linear Discriminant Functions (LDF), Multilinear Principal Component Analysis (MPCA) with K-Nearest Neighbor (KNN) classifier and the third method: MPCA plus Linear Discriminant Analysis (MPCA+LDA) with KNN classifier. Gait samples are fed into the MPCA and MPCALDA algorithms using a novel tensor-based form of the gait images. This arrangement results into nine recognition sub-systems. Decisions from the nine classifiers are fused using decision-level (majority voting) scheme. A comparison between unweighted and weighted voting schemes is also presented. The methods are evaluated on CASIA B Dataset using four different experimental setups, and on OU-ISIR Dataset B using two different setups. The experimental results show that the classification accuracy of the proposed methods is encouraging and outperforms several state-of-the-art gait recognition approaches reported in the literature. Two new gait representations, AFI and EMAEI, are proposed and evaluated.New tensorial gait representation technique is proposed.Energy-based gait approaches can perform very well.Fusion is recommended in gait recognition, especially decision-level fusion.Weighted fusion can generally perform better than unweighted fusion.

Multilinear forms over finite fields are considered. Multilinear forms over a field are products in which each factor is the sum of variables or elements of this field. Each multilinear form defines a function over this field. A multilinear form is called satisfiable if it represents a nonzero function. We show the N P-completeness of the satisfiability recognition problem for multilinear forms over each finite field of q elements for q ≥ 3. A theorem is proved that distinguishes cases of polynomiality and NP-completeness of the satisfiability recognition problem for multilinear fields for each possible q ≥ 3.

Model selection in tensor decomposition is important for real applications if the rank of the original data tensor is unknown and the observed tensor is noisy. In the Tucker model, the minimum description length (MDL) or Bayesian information criteria have been applied to tensors via matrix unfolding, but these methods are sensitive to noise when the tensors have a multilinear low rank structure given by the Tucker model. In this study, we propose new methods for improving the MDL so it is more robust to noise. The proposed methods are justified theoretically by analyzing the “multilinear low-rank structure” of tensors. Extensive experiments including numerical simulations and a real application to image denoising are provided to illustrate the advantages of the proposed methods.

According to the different types and contents of amino acids in green tea, a new method was proposed for green tea classification and quality evaluation based on excitation-emission matrix (EEM) fluorescence spectroscopy coupled with multilinear pattern recognition in this work. Amino acids in green tea samples were first derived with formaldehyde and acetyl acetone solution. Derivatives of green teas were then scanned with a three-dimensional fluorescence spectrometry. Multilinear pattern recognition methods, including multilinear principal component analysis (M-PCA), self-weight alternative trilinear decomposition (SWATLD), and multilinear partial least squares discriminant analysis (N-PLS-DA) methods, were used to decompose the EEM data sets. All of these multilinear pattern recognition methods showed the clustering tendency for five different kinds of green tea. Compared with the other two methods, N-PLS-DA got more accurate and reliable classification result because it made full use of all the fluorescence information of the derivative green tea samples. At the same time, this method also revealed the possibility of evaluating the grade of green tea.

In this paper, we propose a unified Bayesian joint modeling framework for studying association between a binary treatment outcome and a baseline matrix-valued predictor. Specifically, a joint modeling approach relating an outcome to a matrix-valued predictor through a probabilistic formulation of multilinear principal component analysis is developed. This framework establishes a theoretical relationship between the outcome and the matrix-valued predictor, although the predictor is not explicitly expressed in the model. Simulation studies are provided showing that the proposed method is superior or competitive to other methods, such as a two-stage approach and a classical principal component regression in terms of both prediction accuracy and estimation of association; its advantage is most notable when the sample size is small and the dimensionality in the imaging covariate is large. Finally, our proposed joint modeling approach is shown to be a very promising tool in an application exploring the association between baseline electroencephalography data and a favorable response to treatment in a depression treatment study by achieving a substantial improvement in prediction accuracy in comparison to competing methods.

Background and objective It is challenging to conduct real-time identification of myocardial infarction (MI) due to artifact corruption and high dimensionality of multi-lead electrocardiogram (ECG). In the present study, we proposed an automated single-beat MI detection and localization system using dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT) denoising algorithm. Methods After denoising and segmentation of ECG, a fourth-order wavelet tensor (leads × subbands × samples × beats) was constructed based on the discrete wavelet packet transform (DWPT), to represent the features considering the information of inter-beat, intra-beat, inter-frequency, and inter-lead. To reduce the tensor dimension and preserve the intrinsic information, the multilinear principal component analysis (MPCA) was employed. Afterward, 84 discriminate features were fed into a classifier of bootstrap-aggregated decision trees (Treebagger). A total of 78 healthy and 328 MI (6 types) records including 57557 beats were chosen from PTB diagnostic ECG database for evaluation. Results The validation results demonstrated that our proposed MI detection and localization system embedded with Dual-Q TQWT and wavelet packet tensor decomposition outperformed commonly used discrete wavelet transform (DWT), empirical mode decomposition (EMD) denoising methods and vector-based PCA method. With the Treebagger classifier, we obtained an accuracy of 99.98% in beat level and an accuracy of 97.46% in record level training/testing for MI detection. We also achieved an accuracy of 99.87% in beat level and an accuracy of 90.39% in record level for MI localization. Conclusion Altogether, the automated system brings potential improvement in automated detection and localization of MI in clinical practice.

Accurate detection of abnormal myocardium regions is essential for differential diagnosis of cardiovascular disease. However, to achieve this goal by image analysis will significantly increase the burden on radiologists who is already overwhelmed. To ease the time and energy-demanding process and enhance the reproducibility, we proposed a novel framework for automatic abnormal shape detection on left ventricular (LV) using MR images. Our proposed approach utilizes the features obtained by large deformation diffeomorphic metric mapping (LDDMM). To take advantage of 3D structural information, we introduce multilinear principal component analysis (MPCA) in the framework to reduce feature dimensions. Then we combine MPCA with linear discriminant analysis (LDA) to perform differential diagnosis. The performance of proposed framework is evaluated on patients’ images. In the classification of three common cardiovascular diseases, our proposed method outperformed traditional classifiers (Global point signature, Random Forest and XGBoost) with an accuracy of 94%. To further automatically detect the dysfunctional heart regions, we did a comparison on 3D morphology between the diseased subjects and healthy controls and performed an automatic visualization of the abnormal myocadiac regions. In conclusion, our proposed framework reserves the spatial information of the features generated through LDDMM registration and enables the 3D visualization of abnormal regions of LV. With the advance of our method, differential diagnosis is successfully performed on patients with different cardiovascular diseases.

An image retrieval method based on convolution neural network and dimension reduction is proposed in this paper. Convolution neural network is used to extract high-level features of images, and to solve the problem that the extracted feature dimensions are too high and have strong correlation, multilinear principal component analysis is used to reduce the dimension of features. The features after dimension reduction are binary hash coded for fast image retrieval. Experiments show that the method proposed in this paper has better retrieval effect than the retrieval method based on principal component analysis on the e-commerce image datasets., 2018 International Conference on Security, Pattern Analysis, and Cybernetics(SPAC 2018)

The multi-channel sensor data are widely collected during a manufacturing process to detect the variation of product quality. Multi-channel data can provide comprehensive information for the fault diagnosis, while the cross-correlation and redundant information in the data make it difficult to analyze using common methods. In this paper, the tensor structure and characteristics of a multi-channel dataset are investigated. After that, a novel fault diagnosis method is proposed by introducing the multilinear subspace learning algorithm into deep learning technologies. The dimension of the multi-channel data is reduced using the Multilinear Principal Component Analysis that does not destroy the tensor structure. The CNN is then used to extract features and build a classification model for fault diagnosis. The proposed method is compared with existing methods in the case study about a practical multi-operation forging process. Results show that the proposed fault diagnosis method for multi-channel data has superior performance and lower computational cost than existing approaches.

We consider the question of the possibility to recover a multilinear mapping from the restriction to the diagonal of its extension to a Cartesian power of a space.