Deep subspace clustering networks have attracted much attention in clustering, which an auto-encoder non-linearly maps the input data into a latent space, and fully connected layer named self-expressiveness module is introduced to learn affinity matrix via typical regularization term (e.g., sparse or low-rank). However, adopted terms ignore connectivity within each subspace, limiting their perf...

Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved impressive performance due to the powerful representation extracted using neural networks while prioritizing categorical separability. However, self-reconstruction loss of an AE ignores rich useful relation information and might lead indiscriminative representation, which inevitably degrades performance. It is also cha...

We study the underlying structure of data (approximately) generated from a union of independent subspaces. Traditional methods learn only one subspace, failing to discover the multi-subspace structure, while state-of-the-art methods analyze the multi-subspace structure using data themselves as the dictionary, which cannot offer the explicit basis to span each subspace and are sensitive to error...

The case of sources that generate multidimensional signals, filling a subspace of dimensionality K, is considered. Different coordinate axes of the subspace (“subspace channels”) correspond to different signal portions generated by each source, e.g., data from different spectral bands or different modalities may be assigned to different subspace channels. The mixing system that generates observ...

We propose a low-rank transformation-learning framework to robustify subspace clustering. Many high-dimensional data, such as face images and motion sequences, lie in a union of low-dimensional subspaces. The subspace clustering problem has been extensively studied in the literature to partition such highdimensional data into clusters corresponding to their underlying low-dimensional subspaces....

The last few decades have witnessed a great success of subspace learning for face recognition. From principal component analysis (PCA) [43] and Fisher’s linear discriminant analysis [1], a dozen of dimension reduction algorithms have been developed to select effective subspaces for the representation and discrimination of face images [17, 21, 45, 46, 51]. It has demonstrated that human faces, a...