Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework

• URL: http://arxiv.org/abs/2211.02883v1
• Date: Thu, 3 Nov 2022 08:18:27 GMT
• Title: Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework
• Authors: Liangchen Liu, Qiuhong Ke, Chaojie Li, Feiping Nie, Yingying Zhu
• Abstract summary: We formulate a novel clustering model, which exploits the non-negative feature property and incorporates the multi-view information into a unified joint learning framework. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features.
• Score: 74.25493157757943
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Spectral clustering is an effective methodology for unsupervised learning. Most traditional spectral clustering algorithms involve a separate two-step procedure and apply the transformed new representations for the final clustering results. Recently, much progress has been made to utilize the non-negative feature property in real-world data and to jointly learn the representation and clustering results. However, to our knowledge, no previous work considers a unified model that incorporates the important multi-view information with those properties, which severely limits the performance of existing methods. In this paper, we formulate a novel clustering model, which exploits the non-negative feature property and, more importantly, incorporates the multi-view information into a unified joint learning framework: the unified multi-view orthonormal non-negative graph based clustering framework (Umv-ONGC). Then, we derive an effective three-stage iterative solution for the proposed model and provide analytic solutions for the three sub-problems from the three stages. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features. Extensive experiments on three benchmark data sets demonstrate the effectiveness of the proposed method.

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