Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

Object recognition requires a generalization capability to avoid overfitting, especially when
the samples are extremely few. Generalization from limited samples, usually studied under …

Recently deep learning methods have shown significant progress in data clustering tasks.
Deep clustering methods (including distance-based methods and subspace-based …

It is not clear yet why ADAM-alike adaptive gradient algorithms suffer from worse
generalization performance than SGD despite their faster training speed. This work aims to …

The similarity among samples and the discrepancy among clusters are two crucial aspects
of image clustering. However, current deep clustering methods suffer from inaccurate …

We propose a symmetric graph convolutional autoencoder which produces a low-
dimensional latent representation from a graph. In contrast to the existing graph …

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

This work attempts to provide a plausible theoretical framework that aims to interpret modern
deep (convolutional) networks from the principles of data compression and discriminative …

Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved
impressive performance due to the powerful representation extracted using deep neural …