Subspace clustering is an effective method that has been successfully applied to many
applications. Here, we propose a novel subspace clustering model for multi-view data using …

We present a novel deep neural network architecture for unsupervised subspace clustering.
This architecture is built upon deep auto-encoders, which non-linearly map the input data …

Subspace clustering is an important unsupervised clustering approach. It is based on the
assumption that the high-dimensional data points are approximately distributed around …

Taking the assumption that data samples are able to be reconstructed with the dictionary
formed by themselves, recent multiview subspace clustering (MSC) algorithms aim to find a …

Most existing subspace clustering methods hinge on self-expression of handcrafted
representations and are unaware of potential clustering errors. Thus they perform …

Multiple kernel clustering (MKC) optimally utilizes a group of pre-specified base kernels to
improve clustering performance. Among existing MKC algorithms, the recently proposed late …

In this article, we propose a deep extension of sparse subspace clustering, termed deep
subspace clustering with L1-norm (DSC-L1). Regularized by the unit sphere distribution …

In this article, we propose a multiview self-representation model for nonlinear subspaces
clustering. By assuming that the heterogeneous features lie within the union of multiple …

Neural network based clustering methods usually have better performance compared to the
conventional approaches due to more efficient feature extraction. Most of existing deep …

Abstract We introduce the Neural Collaborative Subspace Clustering, a neural model that
discovers clusters of data points drawn from a union of low-dimensional subspaces. In …