To achieve its impressive performance in tasks such as speech perception or object
recognition, the brain extracts multiple levels of representation from the sensory input …

Despite the impressive clustering performance and efficiency in characterizing both the
relationship between the data and cluster structure, most existing graph-based multi-view …

Multiview clustering partitions data into different groups according to their heterogeneous
features. Most existing methods degenerate the applicability of models due to their …

In multiview learning, it is essential to assign a reasonable weight to each view according to
the view importance. Thus, for multiview clustering task, a wise and elegant method should …

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 …

Graph-based approaches have been successful in unsupervised and semi-supervised
learning. In this paper, we focus on the real-world applications where the same instance can …

Detecting coherent groups is fundamentally important for crowd behavior analysis. In the
past few decades, plenty of works have been conducted on this topic, but most of them have …

For many computer vision applications, the data sets distribute on certain low-dimensional
subspaces. Subspace clustering is to find such underlying subspaces and cluster the data …

Successful visual object recognition methods typically rely on training datasets containing
lots of richly annotated images. Annotating object bounding boxes is both expensive and …

We study the problem of object recognition for categories for which we have no training
examples, a task also called zero--data or zero-shot learning. This situation has hardly been …