With the rapid development of science and technology, high-dimensional data have been
widely used in various fields. Due to the complex characteristics of high-dimensional data, it …

Traditional partition-based clustering algorithms, hard or fuzzy version of C-means, could not
deal with high-dimensional data sets effectively as redundant features may impact the …

Abstract Background and Significance: Automatic detection of depression is crucial in
today's fast-paced, depression-prone society. However, the current diagnosis still relies on …

This paper proposes a novel discriminative regression method, called adaptive locality
preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more …

Recently, image feature extraction algorithms based on 2D discriminant local preserving
projection (2DDLPP) algorithms have been successfully applied in many fields. The …

Unsupervised feature selection methods try to select features which can well preserve the
intrinsic structure of data. To represent such structure, conventional methods construct …

In high-dimensional data classification, effectively extracting discriminative features while
eliminating redundancy is crucial for enhancing the performances of classifiers, such as …

The curse of dimensionality and noise corruption are two tough problems that need to be
solved in hyperspectral image (HSI) classification. However, the current feature …

Abstract Recently, Multi-view Discriminant Analysis (MVDA) has been proposed and
achieves good performance in multi-view recognition tasks. However, as an extension of …

Low-rank matrix recovery is a major challenge in machine learning and computer vision,
particularly for large-scale data matrices, as popular methods involving nuclear norm and …