A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

In this paper, we propose a spectral–spatial feature based classification (SSFC) framework
that jointly uses dimension reduction and deep learning techniques for spectral and spatial …

Unsupervised feature selection is one of the efficient approaches to reduce the dimension of
unlabeled high-dimensional data. We present a novel adaptive autoencoder with …

Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …

Unsupervised feature selection always occupies a key position as a preprocessing in the
tasks of classification or clustering due to the existence of extra essential features within high …

This paper studies the subspace segmentation problem. Given a set of data points drawn
from a union of subspaces, the goal is to partition them into their underlying subspaces they …

The enormous volumes of data created and maintained by industries, research institutions
are on the verge of outgrowing its infrastructure. The advancements in the organization's …

摘要稀疏子空间聚类(Sparse subspace clustering, SSC) 是一种基于谱聚类的数据聚类框架.
高维数据通常分布于若干个低维子空间的并上, 因此高维数据在适当字典下的表示具有稀疏性 …

Locally linear embedding (LLE) is one of the most well-known manifold learning methods.
As the representative linear extension of LLE, orthogonal neighborhood preserving …

For robust face recognition tasks, we particularly focus on the ubiquitous scenarios where
both training and testing images are corrupted due to occlusions. Previous low-rank based …