Multiple Partitions Aligned Clustering

Kang, Zhao; Guo, Zipeng; Wang, Siying; Chen, Wenyu; Su, Yuanzhang; Xu, Zenglin

doi:10.48550/arxiv.1909.06008

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…For singleview hash methods, we utilize K-means algorithm to process the generated binary code for clustering and we preserve the best result of each view clustering. Besides, we adopt eight real-value multi-view clustering algorithms as comparing algorithms, including K-means [42], SC [57], Co-regularize [26], AMGL [40], Mul-NMF [58], MLAN [59], mPAC [60], GMC [?]. We utilize the source code from the author's public homepage for comparing.…”

Section: A Experimental Settingsmentioning

confidence: 99%

Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering

Wang¹,

Yao²,

Jiang³

et al. 2023

Preprint

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Unsupervised hashing methods have attracted widespread attention with the explosive growth of large-scale data, which can greatly reduce storage and computation by learning compact binary codes. Existing unsupervised hashing methods attempt to exploit the valuable information from samples, which fails to take the local geometric structure of unlabeled samples into consideration. Moreover, hashing based on autoencoders aims to minimize the reconstruction loss between the input data and binary codes, which ignores the potential consistency and complementarity of multiple sources data. To address the above issues, we propose a hashing algorithm based on autoencoders for multi-view binary clustering, which dynamically learns affinity graphs with low-rank constraints and adopts collaboratively learning between auto-encoders and affinity graphs to learn a unified binary code, called Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering (GCAE). Specifically, we propose a multi-view affinity graphs learning model with low-rank constraint, which can mine the underlying geometric information from multi-view data. Then, we design an encoder-decoder paradigm to collaborate the multiple affinity graphs, which can learn a unified binary code effectively. Notably, we impose the decorrelation and code balance constraints on binary codes to reduce the quantization errors. Finally, we utilize an alternating iterative optimization scheme to obtain the multi-view clustering results. Extensive experimental results on 5 public datasets are provided to reveal the effectiveness of the algorithm and its superior performance over other state-of-theart alternatives.

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Section: A Experimental Settingsmentioning

confidence: 99%

Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering

Wang¹,

Yao²,

Jiang³

et al. 2023

Preprint

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Large-scale Multi-view Subspace Clustering in Linear Time

Kang

Zhou

Zhao

et al. 2019

Preprint

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Multi-View Multiple Clusterings using Deep Matrix Factorization

Wei¹,

Wang²,

Yu³

et al. 2019

Preprint

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Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can only output a single clustering of the data. Due to their multiplicity, multi-view data, can have different groupings that are reasonable and interesting from different perspectives. However, how to find multiple, meaningful, and diverse clustering results from multi-view data is still a rarely studied and challenging topic in multi-view clustering and multiple clusterings. In this paper, we introduce a deep matrix factorization based solution (DMClusts) to discover multiple clusterings. DMClusts gradually factorizes multi-view data matrices into representational subspaces layer-by-layer and generates one clustering in each layer. To enforce the diversity between generated clusterings, it minimizes a new redundancy quantification term derived from the proximity between samples in these subspaces. We further introduce an iterative optimization procedure to simultaneously seek multiple clusterings with quality and diversity. Experimental results on benchmark datasets confirm that DMClusts outperforms state-of-the-art multiple clustering solutions.

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Multiple Partitions Aligned Clustering

Cited by 3 publications

References 16 publications

Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering

Graph-Collaborated Auto-Encoder Hashing for Multi-view Binary Clustering

Large-scale Multi-view Subspace Clustering in Linear Time

Multi-View Multiple Clusterings using Deep Matrix Factorization

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