Multi-view subspace clustering has received widespread attention to effectively fuse multi-
view information among multimedia applications. Considering that most existing …

Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure
information to improve clustering performance. Recently, many anchor-based variants are …

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the
past few years. Researchers manage to boost clustering accuracy from different points of …

Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: they encounter the expensive time …

We consider the problem of discovering novel object categories in an image collection.
While these images are unlabelled, we also assume prior knowledge of related but different …

Clustering is a long-standing important research problem, however, remains challenging
when handling large-scale image data from diverse sources. In this paper, we present a …

We tackle the problem of discovering novel classes in an image collection given labelled
examples of other classes. We present a new approach called AutoNovel to address this …

The clustering methods have recently absorbed even-increasing attention in learning and
vision. Deep clustering combines embedding and clustering together to obtain optimal …

In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed
ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace …

In this paper, we propose a recurrent framework for joint unsupervised learning of deep
representations and image clusters. In our framework, successive operations in a clustering …