In this paper, we study two challenging problems in incomplete multi-view clustering
analysis, namely, i) how to learn an informative and consistent representation among …

In this article, we propose a unified framework to solve the following two challenging
problems in incomplete multi-view representation learning: i) how to learn a consistent …

The Internet-of-Things (IoT) technology is widely used in various fields. In the Earth
observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and …

Multi-view clustering has attracted intensive attention due to the effectiveness of exploiting
multiple views of data. However, most existing multi-view clustering methods only aim to …

The low-rank tensor representation (LRTR) has become an emerging research direction to
boost the multi-view clustering performance. This is because LRTR utilizes not only the …

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

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

Most popular clustering methods map raw image data into a projection space in which the
clustering assignment is obtained with the vanilla k-means approach. In this article, we …

Subspace clustering refers to the problem of segmenting data drawn from a union of
subspaces. State-of-the-art approaches for solving this problem follow a two-stage …

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 …