Unsupervised Single and Multiple Views Feature Extraction with Structured Graph

Zhuge, Wenzhang; Nie, Feiping; Hou, Chenping; Yi, Dongyun

doi:10.1109/tkde.2017.2725263

Cited by 60 publications

(22 citation statements)

References 34 publications

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“…• Unsupervised Multi-View Feature Extraction with Structured Graph (MFESG) [41]. MFESG simultaneously learns a projection matrix and a structured graph containing the clustering information.…”

Section: B Comparative Methodsmentioning

confidence: 99%

Robust Unsupervised Multi-View Feature Learning With Dynamic Graph

Liu

Shi

et al. 2019

IEEE Access

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Graph-based multi-view feature learning methods learn a low-dimensional embedding of the data by modeling the affinity correlations with a graph to reduce the dimension. However, the learned low-dimensional representation relies on a fixed graph that is potentially inaccurate and unreliable. Besides, the graph construction and the projection matrix leaning are separated into two independent processes. To tackle the problems, we propose a robust unsupervised multi-view feature learning method with a dynamic graph. The dynamic graph structure is constructed adaptively and the robust projection matrix is learned simultaneously. Specifically, we adaptively learn a dynamic graph which captures the intrinsic multiple view-specific relations of samples. Robust projection matrix learning suppresses the adverse noises and preserves the intrinsic graph structure. Moreover, the assigned weights are learned automatically for each view without any extra parameter. We finally develop an efficient alternative optimization algorithm to solve the objective formulation. The extensive experiments conducted on several multi-view datasets demonstrate the effectiveness of our proposed method. INDEX TERMS Dynamic graph, feature learning, multi-view learning, robust projection matrix.

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Section: B Comparative Methodsmentioning

confidence: 99%

Robust Unsupervised Multi-View Feature Learning With Dynamic Graph

Liu

Shi

et al. 2019

IEEE Access

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“…Moreover, by virtue of the development of data acquisition and processing technologies, increasing volume of data are represented by multiple views [33]- [37]. For example, a video might consist of text, images, and sounds [38], [39]; an image can be described in different features, e.g., SIFT, GIST, LBP, HoG, and Garbor [40], [41]; a document can be translated into different languages [42], [43].…”

Section: Introductionmentioning

confidence: 99%

Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view

Kang

Lin

Zhu³

et al. 2021

Preprint

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Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: encounter the expensive time overhead, fail in exploring the explicit clusters, and cannot generalize to unseen data points. In this work, we propose a scalable graph learning framework, seeking to address the above three challenges simultaneously. Specifically, it is based on the ideas of anchor points and bipartite graph. Rather than building a n×n graph, where n is the number of samples, we construct a bipartite graph to depict the relationship between samples and anchor points. Meanwhile, a connectivity constraint is employed to ensure that the connected components indicate clusters directly. We further establish the connection between our method and the K-means clustering. Moreover, a model to process multi-view data is also proposed, which is linear scaled with respect to n. Extensive experiments demonstrate the efficiency and effectiveness of our approach with respect to many state-of-the-art clustering methods.

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“…One successful subcategory of multi-view learning is multi-view clustering (MVC), which classifies given subjects into subgroups based on similarities among subjects [6]. Although various approaches have been proposed in this category, graph-based multi-view clustering (GMVC) has recently garnered increasing attention: it has demonstrated state-of-the-art performance for numerous applications [7,8,9,10,11,12,13,14,15]. Fundamentally, GMVC originates from single-view spectral clustering methods.…”

Section: Introductionmentioning

confidence: 99%

Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering

Horie

Kasai

2020

Preprint

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Multi-view data analysis has gained increasing popularity because multi-view data are frequently encountered in machine learning applications. A simple but promising approach for clustering of multi-view data is multi-view clustering (MVC), which has been developed extensively to classify given subjects into some clustered groups by learning latent common features that are shared across multi-view data. Among existing approaches, graph-based multi-view clustering (GMVC) achieves state-of-the-art performance by leveraging a shared graph matrix called the unified matrix. However, existing methods including GMVC do not explicitly address inconsistent parts of input graph matrices. Consequently, they are adversely affected by unacceptable clustering performance. To this end, this paper proposes a new GMVC method that incorporates consistent and inconsistent parts lying across multiple views. This proposal is designated as CI-GMVC. Numerical evaluations of real-world datasets demonstrate the effectiveness of the proposed CI-GMVC 1 .

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Unsupervised Single and Multiple Views Feature Extraction with Structured Graph

Cited by 60 publications

References 34 publications

Robust Unsupervised Multi-View Feature Learning With Dynamic Graph

Robust Unsupervised Multi-View Feature Learning With Dynamic Graph

Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view

Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering

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