State-of-the-art subspace clustering methods are based on self-expressive model, which
represents each data point as a linear combination of other data points. By enforcing such …

Multi-view subspace clustering has made remarkable achievements in the field of multi-view
learning for high-dimensional data. However, many existing multi-view subspace clustering …

Large-scale subspace clustering usually drops the requirements of the full similarity matrix
and Laplacian matrix but constructs the anchor affinity matrix and uses matrix approximation …

The K-subspaces (KSS) method is a generalization of the K-means method for subspace
clustering. In this work, we present local convergence analysis and a recovery guarantee for …

Low dimensional nonlinear structure abounds in datasets across computer vision and
machine learning. Kernelized matrix factorization techniques have recently been proposed …

Clustering noisy, high-dimensional, and structurally complex data have always been a
challenging task. As most existing clustering methods are not able to deal with both the …

We study the problem of clustering validation, ie, clustering evaluation without knowledge of
ground-truth labels, for the increasingly-popular framework known as subspace clustering …

Subspace clustering, the task of clustering high dimensional data when the data points
come from a union of subspaces, is one of the fundamental tasks in unsupervised machine …

State-of-the-art subspace clustering methods are based on convex formulations whose
theoretical guarantees require the subspaces to be low-dimensional. Dual Principal …

Subspace clustering is a powerful generalization of clustering for high-dimensional data
analysis, where low-rank cluster structure is leveraged for accurate inference.-Subspaces …