Yuwu Lu

TL;DR: This paper proposes a novel dimensionality reduction method, named low-rank preserving projections (LRPP) for image classification, and shows the effectiveness and the feasibility of the proposed method with encouraging results.

TL;DR: A robust linear dimensionality reduction technique termed low-rank embedding (LRE) is proposed, which provides a robust image representation to uncover the potential relationship among the images to reduce the negative influence from the occlusion and corruption so as to enhance the algorithm’s robustness in image feature extraction.

TL;DR: This paper proposes a novel nonnegative factorization method, called structurally incoherent low-rank NMF (SILR-NMF), in which they jointly consider structural incoherence and low- rank properties of data for image classification.

TL;DR: A novel NPP method called low-rank 2DNPP (LR-2DNPP) is proposed, which encodes the structural incoherence of the learned clean data to enhance the discriminative ability for feature extraction and uses seven public image databases to verify the performance of the proposed methods.

TL;DR: A novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification that integrates the nonnegative constraint, orthogonality, and discriminant information in the objective function and is proposed to enhance the discriminant ability of the learned base matrix.