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A new evaluation scheme for double exposure three-dimensional particle-tracking velocimetry is proposed. Its main feature, a robust multi-pass matching algorithm, is presented and validated by investigating its performance when applied to a synthetic data set. To evaluate real measurement data, the approach is supplemented by an iterative triangulation scheme, in which the resulting particle positions are validated through the matching algorithm. The comparison with tomographic particle-image velocimetry data shows good agreement. The proposed algorithm allows this approach to be applied to volumetric measurements with seeding densities exceeding standard particle-tracking applications. Therefore, it can serve as a drop-in replacement for tomographic particle-image velocimetry at significantly reduced computational cost. [ABSTRACT FROM AUTHOR]

Machine learning methods have gained increased popularity in biomedical research during the recent years. However, very few of them support the analysis of longitudinal data, where several samples are collected from an individual over time. Additionally, most of the available longitudinal machine learning methods assume that the measurements are aligned in time, which is often not the case in real data. Here, we introduce a robust longitudinal machine learning method, named likelihood contrasts (LC), which supports study designs with unaligned time points. Our LC method is a binary classifier, which uses linear mixed models for modelling and log-likelihood for decision making. To demonstrate the benefits of our approach, we compared it with existing methods in four simulated and three real data sets. In each simulated data set, LC was the most accurate method, while the real data sets further supported the robust performance of the method. LC is also computationally efficient and easy to use. [ABSTRACT FROM AUTHOR]

The minimum covariance determinant (MCD) method is a highly robust estimator of multivariate location and scatter, for which a fast algorithm is available. Since estimating the covariance matrix is the cornerstone of many multivariate statistical methods, the MCD is an important building block when developing robust multivariate techniques. It also serves as a convenient and efficient tool for outlier detection. The MCD estimator is reviewed, along with its main properties such as affine equivariance, breakdown value, and influence function. We discuss its computation, and list applications and extensions of the MCD in applied and methodological multivariate statistics. Two recent extensions of the MCD are described. The first one is a fast deterministic algorithm which inherits the robustness of the MCD while being almost affine equivariant. The second is tailored to high‐dimensional data, possibly with more dimensions than cases, and incorporates regularization to prevent singular matrices. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical and Graphical Methods of Data Analysis > Robust Methods Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of personalized ranking from implicit feedback (PRIF). It is a more common scenario (e.g. purchase history, click log and page visitation) in recommender systems. The training data are only binary in these problems, reflecting the users' actions or inactions. One shortcoming of previous PRIF algorithms is noise sensitivity: outliers in training data might bring significant fluctuations in the training process and lead to inaccuracy of the algorithm. In this paper, we propose two robust PRIF algorithms to solve the noise sensitivity problem of existing PRIF algorithms by using the pairwise sigmoid and pairwise fidelity loss functions. These two pairwise loss functions are flexible and can easily be adopted by popular collaborative filtering models such as the matrix factorization (MF) model and the K-nearest-neighbor (KNN) model. A learning process based on stochastic gradient descent with bootstrap sampling is utilized for the optimization. Experiments are conducted on practical datasets containing noisy data points or outliers. Results demonstrate that the proposed algorithms outperform several state-of-the-art one class collaborative filtering (OCCF) algorithms on both the MF and KNN models over different evaluation metrics. [ABSTRACT FROM AUTHOR]

In this paper, a new robust data clustering algorithm inspired by Newtonian law of gravity is proposed. The proposed algorithm not only reduces the effects of noise and outliers but also, it is not sensible to the initial positions of the centroids. In the proposed method, data points and the cluster centroids are considered as fixed celestial objects and movable objects, respectively. The celestial objects apply a gravity force to the movable objects and change their positions in the feature space and therefore, the best positions of the cluster centroids are determined by employing the law of gravity. To evaluate the performance of the proposed algorithm, a comparative experimental study with some well-known clustering algorithms, using three visual datasets as well as several benchmark datasets from UCI, is performed. The experimental results confirm the effectiveness and the efficiency of the proposed clustering algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a robust real-time hand tracking system via multi-cue integration. In practice, the motion information of the hand, such as optical flow, is hard to exploit, because images of hands lack texture. As a result, the integration of the color and motion cues using conventional integration algorithms is difficult. Here, we integrate the motion and color cues from a novel feature point selection view. The hand is tracked using feature points, and the integration is realized during the feature points generation and selection process. In the generation process, a bounding box estimated by the color cue is used to provide a region for the feature points generation. Then, the RCD ( Representative , Compact and Diverse ) criteria are proposed to control the feature point selection process. After the selection process, the feature points are tracked using estimates of the motion of each feature point. The centroid of the feature points in each frame is adopted as the position of the hand. The experimental results show that our integration algorithm outperforms tracking algorithms that only use a single cue. Also the proposed tracking algorithm is more robust in complex environments than other state-of-art algorithms. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a robust algorithm is proposed for reconstructing 2D curve from unorganized point data with a high level of noise and outliers. By constructing the quadtree of the input point data, we extract the “grid-like” boundaries of the quadtree, and smooth the boundaries using a modified Laplacian method. The skeleton of the smoothed boundaries is computed and thereby the initial curve is generated by circular neighboring projection. Subsequently, a normal-based processing method is applied to the initial curve to smooth jagged features at low curvatures areas, and recover sharp features at high curvature areas. As a result, the curve is reconstructed accurately with small details and sharp features well preserved. A variety of experimental results demonstrate the effectiveness and robustness of our method. [Copyright &y& Elsevier]

Abstract: This paper presents an automatic approach to reconstruct human motion using noisy depth data from multiple views. Although multi-view constraints are provided by this setup, it still exhibits great challenges to robustly reconstruct dynamic human performances due to inherent complexity and self-occlusion of human motion. In the insight that the semantics of human motion will supply strong prior in motion reconstruction, we therefore propose a SCAPE-based motion reconstruction algorithm. As the building blocks of this main algorithm, we (1) re-train a SCAPE model based on an expanded human pose database containing human poses collected from different databases to enlarge the tracking space, (2) develop a correspondence estimation method based on articulated ICP to improve the robustness of SCAPE tracking. We conduct experiments to demonstrate the effectiveness of our method, and show that our system is able to capture and reconstruct accurate human motion. [Copyright &y& Elsevier]

Abstract: The trust region method which originated from the Levenberg–Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed. [Copyright &y& Elsevier]

Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. [ABSTRACT FROM PUBLISHER]

Abstract: This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as “partial labeling” of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint framework and derive the corresponding EM updates accordingly. In our simulations, we observe that using this side information, we considerably improve the state recognition performance, up to 70%, with respect to the “achievable margin” defined by the baseline algorithms. Moreover, our algorithm is shown to be robust to the training conditions. [Copyright &y& Elsevier]

This paper proposes a novel method to the problem of non-orthogonality of features obtained using the Non-negative Matrix Factorization (NMF) method. For any given non-negative data matrix, the NMF method provides a learned local representation by approximating the data matrix as a product of two non-negative matrices. However, the non-orthogonality of the features hinders the effective use of the learned representation from the NMF. To overcome this problem, we propose the following steps: calculate the metric in the feature space adapted to the features, apply the Cholesky decomposition to the metric and identify the upper triangular matrix, and use the upper triangular matrix as a linear mapping for the learned representation from the NMF. The proposed method is applied to current NMF-based clustering algorithms and evaluated over real-world datasets. The results indicate that the proposed method improves the performance of those algorithms, and that the method is very robust, even in the presence of a large number of features. [ABSTRACT FROM AUTHOR]

Abstract: Data weighting is of paramount importance with respect to classification performance in pattern recognition applications. In this paper, the output labels of datasets have been encoded using binary codes (numbers) and by this way provided a novel data weighting method called binary encoded output based data weighting (BEOBDW). In the proposed data weighting method, first of all, the output labels of datasets have been encoded with binary codes and then obtained two encoded output labels. Depending to these encoded outputs, the data points in datasets have been weighted using the relationships between features of datasets and two encoded output labels. To generalize the proposed data weighting method, five datasets have been used. These datasets are chain link (2 classes), two spiral (2 classes), iris (3 classes), wine (3 classes), and dermatology (6 classes). After applied BEOBDW to five datasets, the k-NN (nearest neighbor) classifier has been used to classify the weighted datasets. A set of experiments on used real world datasets demonstrated that the proposed data weighting method is a very efficient and has robust discrimination ability in the classification of datasets. BEOBDW method could be confidently used before many classification algorithms. [Copyright &y& Elsevier]

Abstract: We present an efficient and robust algorithm for the landmark transfer on 3D meshes that are approximately isometric. Given one or more custom landmarks placed by the user on a source mesh, our method efficiently computes corresponding landmarks on a family of target meshes. The technique is useful when a user is interested in characterization and reuse of application-specific landmarks on meshes of similar shape (for example, meshes coming from the same class of objects). Consequently, across a set of multiple meshes consistency is assured among landmarks, regardless of landmark geometric distinctiveness. The main advantage of our method over existing approaches is its low computation time. Differently from existing non-rigid registration techniques, our method detects and uses a minimum number of geometric features that are necessary to accurately locate the user-defined landmarks and avoids performing unnecessary full registration. In addition, unlike previous techniques that assume strict consistency with respect to geodesic distances, we adopt histograms of geodesic distance to define feature point coordinates, in order to handle the deviation of isometric deformation. This allows us to accurately locate the landmarks with only a small number of feature points in proximity, from which we build what we call a minimal graph. We demonstrate and evaluate the quality of transfer by our algorithm on a number of Tosca data sets. [Copyright &y& Elsevier]

Abstract: The Morse–Smale (MS) complex has proven to be a useful tool in extracting and visualizing features from scalar-valued data. However, existing algorithms to compute the MS complex are restricted to either piecewise linear or discrete scalar fields. This paper presents a new combinatorial algorithm to compute MS complexes for two-dimensional piecewise bilinear functions defined on quadrilateral meshes. We derive a new invariant of the gradient flow within a bilinear cell and use it to develop a provably correct computation, unaffected by numerical instabilities. This includes a combinatorial algorithm to detect and classify critical points as well as a way to determine the asymptotes of cell-based saddles and their intersection with cell edges. Finally, we introduce a simple data structure to compute and store integral lines on quadrilateral meshes which by construction prevents intersections and allows to enforce constraints on the gradient flow that preserve known invariants. [Copyright &y& Elsevier]

Derivative-free optimization focuses on designing methods to solve optimization problems without the analytical knowledge of the function. In this paper, we consider the problem of designing derivative-free methods for finite minimax problems: minxmaxi=1, 2, …, Nfi(x). In order to solve the problem efficiently, we seek to exploit the smooth substructure within the problem. Using ideas developed by Burkeet al.[J.V. Burke, A.S. Lewis, and M.L. Overton,Approximating subdifferentials by random sampling of gradients, Math. Oper. Res. 27(3) (2002), pp. 567–584; J.V. Burke, A.S. Lewis, and M.L. Overton,A robust gradient sampling algorithm for nonsmooth, nonconvex optimization, SIAM J. Optim. 15(3) (2005), pp. 751–779 (electronic)], we create the idea of a robust simplex gradient descent direction and use it to accelerate convergence. Convergence is proven by showing that the resulting algorithm fits into the directional direct-search framework. Numerical tests demonstrate the algorithm's effectiveness on finite minimax problems. [ABSTRACT FROM AUTHOR]

Abstract: We address the challenging problem of face recognition under the scenarios where both training and test data are possibly contaminated with spatial misalignments. A supervised sparse coding framework is developed in this paper towards a practical solution to misalignment-robust face recognition. Each gallery face image is represented as a set of patches, in both original and misaligned positions and scales, and each given probe face image is then uniformly divided into a set of local patches. We propose to sparsely reconstruct each probe image patch from the patches of all gallery images, and at the same time the reconstructions for all patches of the probe image are regularized by one term towards enforcing sparsity on the subjects of those selected patches. The derived reconstruction coefficients by -norm minimization are then utilized to fuse the subject information of the patches for identifying the probe face. Such a supervised sparse coding framework provides a unique solution to face recognition with all (Here, we emphasize “all” because some conventional algorithms for face recognition possess partial of these characteristics.) the following four characteristics: (1) the solution is model-free, without the model learning process, (2) the solution is robust to spatial misalignments, (3) the solution is robust to image occlusions, and (4) the solution is effective even when there exist spatial misalignments for gallery images. Extensive face recognition experiments on three benchmark face datasets demonstrate the advantages of the proposed framework over holistic sparse coding and conventional subspace learning based algorithms in terms of robustness to spatial misalignments and image occlusions. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new road lane model based on linear fuzzy space mathematics, coupled with a robust road lane detection method using fuzzy c-means clustering. The fuzzy line based road lane model presented here describes a lane as a set of fuzzy collinear fuzzy points. The proposed algorithm for road line detection is able to deal with imprecise data and enables reduced computational complexity (proportional to the number of fuzzy points multiplied by the number of fuzzy lines) versus a standard Hough transformation. Experimental results show that the proposed method is fast, and robust enough for use in real-time applications. The proposed method has been implemented as an Android-based mobile phone application. [Copyright &y& Elsevier]

Abstract: Accurate estimation of mass transport parameters is necessary for overall design and evaluation processes of the waste disposal facilities. The mass transport parameters, such as effective diffusion coefficient, retardation factor and diffusion accessible porosity, are estimated from observed diffusion data by inverse analysis. Recently, particle swarm optimization (PSO) algorithm has been used to develop inverse model for estimating these parameters that alleviated existing limitations in the inverse analysis. However, PSO solver yields different solutions in successive runs because of the stochastic nature of the algorithm and also because of the presence of multiple optimum solutions. Thus the estimated mean solution from independent runs is significantly different from the best solution. In this paper, two variants of the PSO algorithms are proposed to improve the performance of the inverse analysis. The proposed algorithms use perturbation equation for the gbest particle to gain information around gbest region on the search space and catfish particles in alternative iterations to improve exploration capabilities. Performance comparison of developed solvers on synthetic test data for two different diffusion problems reveals that one of the proposed solvers, CPPSO, significantly improves overall performance with improved best, worst and mean fitness values. The developed solver is further used to estimate transport parameters from 12 sets of experimentally observed diffusion data obtained from three diffusion problems and compared with published values from the literature. The proposed solver is quick, simple and robust on different diffusion problems. [Copyright &y& Elsevier]

Abstract: Peak-fitting of X-ray photoelectron spectroscopy (XPS) data can be very sensitive to noise when the difference on the binding energy among the peaks is smaller than the width of the peaks. This sensitivity depends on the fitting algorithm. Angle-resolved XPS (ARXPS) analysis offers the opportunity of employing the combined information contained in the data at the various angles to reduce the sensitivity to noise. The assumption of shared peak parameters (center and width) among the spectra for the different angles, and how it is introduced into the analysis, plays a basic role. Sequential fitting is the usual practice in ARXPS data peak-fitting. It consist on first estimating the center and width of the peaks from the data acquired at one of the angles, and then using those parameters as a starting approximation for fitting the data for each of the rest of the angles. An improvement of this method consists of averaging the centers and widths of the peaks obtained at the different angles, and then employing these values to assess the areas of the peaks for each angle. Another strategy for using the combined information is by assessing the peak parameters from the sum of the experimental data. The complete use of the combined information contained in the data-set is optimized by the simultaneous fitting method. It consists of the assessment of the center and width of the peaks by fitting the data at all the angles simultaneously. Computer-generated data was employed to compare the sensitivity with respect to noise between the sequential, averaged-sequential, sum, and simultaneous fitting methods. It is shown that the latter is significantly more robust and could provide reliable results even for noisy data and small peak separation. The fundamentals for the robustness of the simultaneous method are discussed, as well as the possibility of fitting many parameters at the same time. As an added feature of ARXPS, it was found that the estimation of the error intervals on the peak parameters is done remarkably more precisely by employing XPS data at various angles. [Copyright &y& Elsevier]

Abstract: In an asymmetrical interval data set, the error ranges of the upper and lower interval ends are different. This situation is common in practice because of the usual presence of uncertain influences. In prior “crisp input and interval output” regression analysis, a crude symmetrical estimation is obtained, and the asymmetrical character of training data cannot be depicted exactly. In this paper, an asymmetrical interval data analysis is proposed for the first time. The two interval ends are studied independently, and a set of regression models and -insensitive functions are proposed to strengthen the description of the interval ends. The support vector machine (SVM) is imported into this approach (for its model-free character in nonlinear regression) and further extended by -insensitive functions to the extended -SVM. A robust algorithm is presented to eliminate the effect of outliers. Experiments are then presented to verify the quality of performance of the extended -SVM. Advantages over other approaches are considered in the conclusion. [Copyright &y& Elsevier]