Deep Adversarial Inconsistent Cognitive Sampling for Multi-view Progressive Subspace Clustering

by   Renhao Sun, et al.

Deep multi-view clustering methods have achieved remarkable performance. However, all of them failed to consider the difficulty labels (uncertainty of ground-truth for training samples) over multi-view samples, which may result into a nonideal clustering network for getting stuck into poor local optima during training process; worse still, the difficulty labels from multi-view samples are always inconsistent, such fact makes it even more challenging to handle. In this paper, we propose a novel Deep Adversarial Inconsistent Cognitive Sampling (DAICS) method for multi-view progressive subspace clustering. A multiview binary classification (easy or difficult) loss and a feature similarity loss are proposed to jointly learn a binary classifier and a deep consistent feature embedding network, throughout an adversarial minimax game over difficulty labels of multiview consistent samples. We develop a multi-view cognitive sampling strategy to select the input samples from easy to difficult for multi-view clustering network training. However, the distributions of easy and difficult samples are mixed together, hence not trivial to achieve the goal. To resolve it, we define a sampling probability with theoretical guarantee. Based on that, a golden section mechanism is further designed to generate a sample set boundary to progressively select the samples with varied difficulty labels via a gate unit, which is utilized to jointly learn a multi-view common progressive subspace and clustering network for more efficient clustering. Experimental results on four real-world datasets demonstrate the superiority of DAICS over the state-of-the-art methods.



page 1

page 11


Enriched Robust Multi-View Kernel Subspace Clustering

Subspace clustering is to find underlying low-dimensional subspaces and ...

Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering

Multi-view subspace clustering aims to divide a set of multisource data ...

Deep Multi-view Semi-supervised Clustering with Sample Pairwise Constraints

Multi-view clustering has attracted much attention thanks to the capacit...

Multi-view Information-theoretic Co-clustering for Co-occurrence Data

Multi-view clustering has received much attention recently. Most of the ...

Robust Localized Multi-view Subspace Clustering

In multi-view clustering, different views may have different confidence ...

Error-Robust Multi-View Clustering

In the era of big data, data may come from multiple sources, known as mu...

Progressive Cluster Purification for Unsupervised Feature Learning

In unsupervised feature learning, sample specificity based methods ignor...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.