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The classification of cancer subtypes is substantial for the diagnosis and treatment of cancer. However, the gene expression data used for cancer subtype classification are high dimensional in nature and small in sample size. In this paper, an efficient dimensionality reduction with optimized long short term memory, algorithm (OLSTM) is used for gene expression data classification. The main three stages of the proposed method are explicitly pre-processing, dimensional reduction, and gene expression data classification. In the pre-processing method, the missing values and redundant values are removed for high-quality data. Following, the dimensional reduction is done by orthogonal locality preserving projections (OLPP). Finally, gene classification is done by an OLSTM classifier. Here the traditional long short term memory (LSTM) is modified using parameter optimization which uses the adaptive artificial flora optimization (AAFO) algorithm. Based on the migration and flora reproduction process, the AAFO algorithm is stimulated. Using the accuracy, sensitivity, specificity, precision, recall, and fmeasure, the proposed performance is analyzed. The test outcomes illustrate the effectiveness of the gene expression data classification with a 94.19% of accuracy value. The proposed gene expression data classification is implemented in the MATLAB platform. [ABSTRACT FROM AUTHOR]

Aiming at the problem that accuracy of orthogonal locality preserving projections（OLPP） for fault diagnosis is not high enough,a fault diagnosis method based on semi-supervised neighborhood adaptive orthogonal locality preserving projections（SSNA-OLPP） for dimension reduction is proposed.In this method,fault features that can represent the fault state is firstly constructed based on local characteristic-scale decomposition（LCD） and time-frequency domain feature.And then,the SSNA-OLPP is used to compress the high-dimension feature into low-dimension feature which has better discrimination.Finally,the low-dimension feature are input support vector machine（SVM） to identification fault.SSNA-OLPP can adaptive adjust the neighborhood with the guidance of local cluster coefficient,at the same time,information of some labeled samples are also used to adjust the weight matrix among all samples in the original characteristic space,as a result,better fault diagnosis accuracy can achieved.The experiment results of rolling bearing fault diagnosis verified the effectiveness of the method.

Image denoising approaches that learn spatially adaptive dictionaries from the observed noisy image have gathered a lot of attention in the past decade. These methods rely on the hypothesis that patches from the underlying clean image can be expressed as sparse linear combinations of these dictionary vectors (bases). We present a framework for inferring an orthonormal set of dictionary vectors using orthogonal locality preserving projection (OLPP). This ensures that patches that are similar in the noisy image should produce similar coefficients when projected in the OLPP domain. Unlike other projection methods, the locality preserving property of OLPP automatically groups similar patches together during inference of the basis. Hence, only one global orthonormal basis suffices to sparsely represent patches from a large subimage or a large portion of the image. The proposed amalgamation of the sparsity and global dictionary make the current approach more suitable for an image denoising task with reduced computational complexity. Experiments on several benchmark datasets made it clear that the proposed method is capable of preserving fine textures while denoising an image, on par with or surpassing several state-of-the-art methods for gray-scale and color images. [ABSTRACT FROM AUTHOR]

Face image data taken with various capturing devices are usually high dimensional and not very suitable for accurate classification. Recently, a lot of manifold learning algorithms have been used in face recognition community. Among them, locality preserving projections (LPP) is one of the most promising feature extraction techniques. In this paper, a new face recognition method based on orthogonal discriminant locality preserving projections (ODLPP) is proposed. Based on LPP, ODLPP takes into account the between-class information, changes the objective function, and then orthogonalizes the basis vectors of the face subspace. The proposed method was compared with eigenface, Fisherface, orthogonal LPP (OLPP) and Laplacianface methods on the Yale and AR face databases. Experimental results indicated the promising performance of the proposed method. [Copyright &y& Elsevier]