Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints

• URL: http://arxiv.org/abs/2206.04949v2
• Date: Fri, 5 May 2023 06:43:34 GMT
• Title: Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints
• Authors: Rui Chen, Yongqiang Tang, Wensheng Zhang, Wenlong Feng
• Abstract summary: We propose a novel Deep Multi-view Semi-supervised Clustering (DMSC) method, which jointly optimize three kinds of losses during networks finetuning. We demonstrate that our proposed approach performs better than the state-of-the-art multi-view and single-view competitors.
• Score: 10.226754903113164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally overlook the significance of weakly-supervised information and fail to preserve the feature properties of multiple views, thus resulting in unsatisfactory clustering performance. To address these issues, in this paper, we propose a novel Deep Multi-view Semi-supervised Clustering (DMSC) method, which jointly optimizes three kinds of losses during networks finetuning, including multi-view clustering loss, semi-supervised pairwise constraint loss and multiple autoencoders reconstruction loss. Specifically, a KL divergence based multi-view clustering loss is imposed on the common representation of multi-view data to perform heterogeneous feature optimization, multi-view weighting and clustering prediction simultaneously. Then, we innovatively propose to integrate pairwise constraints into the process of multi-view clustering by enforcing the learned multi-view representation of must-link samples (cannot-link samples) to be similar (dissimilar), such that the formed clustering architecture can be more credible. Moreover, unlike existing rivals that only preserve the encoders for each heterogeneous branch during networks finetuning, we further propose to tune the intact autoencoders frame that contains both encoders and decoders. In this way, the issue of serious corruption of view-specific and view-shared feature space could be alleviated, making the whole training procedure more stable. Through comprehensive experiments on eight popular image datasets, we demonstrate that our proposed approach performs better than the state-of-the-art multi-view and single-view competitors.

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