Multi-View Multiple Clustering

IEEE Trans. Neural Netw. Learning Syst.

et al. 2022

Self Cite

Multi-view multi-instance multi-label learning (M3L) is a framework for modeling complex objects. In this framework, each object (or bag) contains one or more instances, is represented with different feature views, and simultaneously annotated with a set of non-exclusive semantic labels. Given the multiplicity of the studied objects, traditional M3L methods generally demand a large number of labeled bags to train a predictive model to annotate bags (or instances) with semantic labels. However, annotating sufficient bags is very expensive and often impractical. In this paper, we present an Active learning based M3L approach (M3AL) to reduce the labeling costs of bags and to improve the performance as much as possible. M3AL firstly adapts the multi-view self-representation learning to evacuate the shared and individual information of bags and to learn the shared/individual similarities between bags across/within views. Next, to avoid scrutinizing all the possible labels, M3AL introduces a new query strategy that leverages the shared and individual information, and the diverse instance distribution of bags across views, to select the most informative bag-label pair for query. Experimental studies on benchmark datasets show that M3AL can significantly reduce the query costs while achieve a better performance than other related competitive methods at the same cost.

Section: B Active Learning With Mvmlmentioning

confidence: 99%

Section: Selecting Informative Bag-label Pair Across Viewsmentioning

confidence: 99%

Multiview Multi-Instance Multilabel Active Learning

Xing

IEEE Trans. Neural Netw. Learning Syst.

et al. 2022

Self Cite

“…To find multiple clusterings on multi-view data, (Yao et al 2019b) recently proposed a solution called multi-view multiple clustering (MVMC). MVMC extracts the individual and shared similarity matrices of multi-view data based on the adapted self-representation learning (Luo et al 2018), and then applies semi-nonnegative matrix factorization (Ding, Li, and Jordan 2010) on each combination of the individual and common similarity data matrices to generate alternative clusterings, where the quality is pursued by the commonality matrix and the diversity is obtained by the individuality matrix.…”

Section: Factorization Factorization and Diversity Controlmentioning

confidence: 99%

“…(ii) DMClusts introduces a balanced redundancy quantification term, which jointly considers the case that two samples are often nearby in the representational subspace per layer, and the reverse case that they are often faraway per layer, to comprehensively quantify the redundancy of multiple clusterings, whilst existing similar quantification overlooks the latter case. Extensive experiments on benchmark datasets show that DM-Clusts significantly outperforms other related competitive multiple clusterings solutions (Yao et al 2019b;Wang et al 2019;Yang and Zhang 2017;Ye et al 2016;Jain, Meka, and Dhillon 2008;Cui, Fern, and Dy 2007) and the deep matrix factorization (Trigeorgis et al 2017) in finding multiple clusterings with quality and diversity.…”

Section: Factorization Factorization and Diversity Controlmentioning

confidence: 99%

Multi-View Multiple Clusterings Using Deep Matrix Factorization

Wei

et al. 2020

AAAI

Self Cite

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.

“…In the past decade, multi-view clustering has become a hot research topic in data mining and machine learning, due to the rapid emergence of a great deal of multi-view data from different areas (Xu, Tao, and Xu 2013;Xu, Wang, and Lai 2016;Chao, Sun, and Bi 2017;Tao et al 2018;Li et al 2018;Huang, Chao, and Wang 2019;Xing et al 2019;Wang et al 2019;Yao et al 2019;Huang, Wang, and Chao 2019b). In multi-view data, the same instance is represented by multiple views obtaining from multiple sources or different feature subsets (Ji et al 2019;Huang, Wang, and Chao 2019c).…”

Section: Introductionmentioning

confidence: 99%

Multi-View Clustering in Latent Embedding Space

Chen

Huang

et al. 2020

AAAI

170

Previous multi-view clustering algorithms mostly partition the multi-view data in their original feature space, the efficacy of which heavily and implicitly relies on the quality of the original feature presentation. In light of this, this paper proposes a novel approach termed Multi-view Clustering in Latent Embedding Space (MCLES), which is able to cluster the multi-view data in a learned latent embedding space while simultaneously learning the global structure and the cluster indicator matrix in a unified optimization framework. Specifically, in our framework, a latent embedding representation is firstly discovered which can effectively exploit the complementary information from different views. The global structure learning is then performed based on the learned latent embedding representation. Further, the cluster indicator matrix can be acquired directly with the learned global structure. An alternating optimization scheme is introduced to solve the optimization problem. Extensive experiments conducted on several real-world multi-view datasets have demonstrated the superiority of our approach.