A panoply of multi-view clustering algorithms has been developed to deal with prevalent
multi-view data. Among them, spectral clustering-based methods have drawn much attention …

Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …

Among various physiological signal acquisition methods for the study of the human brain,
EEG (Electroencephalography) is more effective. EEG provides a convenient, non-intrusive …

We propose two nuclear-and L2, 1-norm regularized 2D neighborhood preserving
projection (2DNPP) methods for extracting representative 2D image features. 2DNPP …

Recently, a prevailing trend of user generated content (UGC) on social media sites is the
emerging micro-videos. Microvideos afford many potential opportunities ranging from …

Recently, many ℓ 1-norm-based PCA approaches have been developed to improve the
robustness of PCA. However, most existing approaches solve the optimal projection matrix …

In this paper, we mainly propose a semi-supervised local multi-manifold Isomap learning
framework by linear embedding, termed SSMM-Isomap, that can apply the labeled and …

As one of the most popular nonlinear dimensionality reduction methods, Isomap has been
widely used in pattern recognition and machine learning. However, Isomap has the …

Convolutional Neural Networks (CNNs) are powerful for image representation, but the
convolution operation may be influenced and degraded by the included noise, and the deep …

The projective dictionary pair learning (DPL) model jointly seeks a synthesis dictionary and
an analysis dictionary by extracting the block-diagonal coefficients with an incoherence …