Subspace clustering methods have been extensively studied in recent years. For 2-
dimensional (2D) data, existing subspace clustering methods usually convert 2D examples …

Subspace clustering aims at discovering the intrinsic structure of data in unsupervised
fashion. As ever in most of approaches, an affinity matrix is constructed by learning from …

Infrared image segmentation is a challenging task, due to interference of complex
background and appearance inhomogeneity of foreground objects. A critical defect of fuzzy …

The kernel subspace clustering algorithm aims to tackle the nonlinear subspace model. The
block diagonal representation subspace clustering has a more promising capability in …

Deep subspace clustering networks have attracted much attention in subspace clustering, in
which an auto-encoder non-linearly maps the input data into a latent space, and a fully …

Unmanned aerial vehicles (UAVs) have been used in a wide range of applications and
become an increasingly important radar target. To better model radar data and to tackle the …

Due to the corruptions or noises that existed in real-world data sets, the affinity graphs
constructed by the classical spectral clustering-based subspace clustering algorithms may …

Constructing tensor with low-rank prior is the crucial issue of tensor based multi-view
subspace clustering methods, but there are still some shortcomings. First, they cannot …

Many aerial images with similar appearances have different but correlated scene labels,
which causes the label ambiguity. Label distribution learning (LDL) can express label …

The aim of the subspace clustering is to segment the high-dimensional data into the
corresponding subspace. The structured sparse subspace clustering and the block diagonal …