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 …

Most learning approaches treat dimensionality reduction (DR) and clustering separately (ie,
sequentially), but recent research has shown that optimizing the two tasks jointly can …

In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method,
which clusters data points with latent representation and simultaneously explores underlying …

Previous multi-view clustering algorithms mostly partition the multi-view data in their original
feature space, the efficacy of which heavily and implicitly relies on the quality of the original …

Existing subspace clustering methods typically employ shallow models to estimate
underlying subspaces of unlabeled data points and cluster them into corresponding groups …

Subspace clustering methods based on data self-expression have become very popular for
learning from data that lie in a union of low-dimensional linear subspaces. However, the …

Subspace clustering aims to cluster unlabeled samples into multiple groups by implicitly
seeking a subspace to fit each group. Most of existing methods are based on a shallow …

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 …