Ratio Sum Versus Sum Ratio for Linear Discriminant Analysis

Dimension reduction is a critical technology for high-dimensional data processing, where Linear Discriminant Analysis (LDA) and its variants are effective supervised methods. However, LDA prefers t…

Fast Locality Discriminant Analysis With Adaptive Manifold Embedding

Linear discriminant analysis (LDA) has been proven to be effective in dimensionality reduction. However, the performance of LDA depends on the consistency assumption of the global structure and the…

Adaptive Neighborhood Metric Learning

In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining. Since the inseparable samples are often mixed with hard samples, cu…

Coordinate Descent Method for k-means

k -means method using Lloyd heuristic is a traditional clustering method which has played a key role in multiple downstream tasks of machine learning because of its simplicity. However, Lloyd heuri…

Truncated Robust Principle Component Analysis With A General Optimization Fra…

Recently, several robust principle component analysis (RPCA) models have been proposed to improve the robustness of principle component analysis (PCA). But an important problem that the robustness …

Large Graph Clustering With Simultaneous Spectral Embedding and Discretization

Spectral clustering methods are gaining more and more interests and successfully applied in many fields because of their superior performance. However, there still exist two main problems to be sol…

Towards Robust Discriminative Projections Learning via Non-Greedy ℓ2,1-Norm…

Linear Discriminant Analysis (LDA) is one of the most successful supervised dimensionality reduction methods and has been widely used in many real-world applications. However, l 2 ℓ2-norm is empl…

Detecting Coherent Groups in Crowd Scenes by Multiview Clustering

On the eigenvectors of p-Laplacian