Low-Rank Representation (LRR) highly suffers from discarding the locality information of data points in subspace clustering, as it may not incorporate structure nonlinearity and non-uniform distribution observations over ambient space. Thus, observational density is lost by state-of-art LRR models, they take a constant number adjacent neighbors into account. This, result, degrades clustering ac...

Incomplete multi-view clustering (IMVC) has attracted remarkable attention due to the emergence of data with missing views in real applications. Recent methods attempt recover information address IMVC problem. However, they generally cannot fully explore underlying properties and correlations similarities across views. This paper proposes a novel Enhanced Tensor Low-rank Sparse Representation R...

Hyperspectral anomaly detection (HAD) as a special target can automatically locate objects whose spectral information are quite different from their surroundings, without any prior about background and anomaly. In recent years, HAD methods based on the low rank representation (LRR) model have caught much attention, achieved good results. However, LRR is global structure model, which inevitably ...

Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems. In this paper, we propose using a log-determinant (LogDet) function as a smoo...