Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours

Nie, Feiping; Cai, Guohao; Li, Xuelong

doi:10.1609/aaai.v31i1.10909

Cited by 316 publications

(56 citation statements)

References 16 publications

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“…Seven widely-used metrics are adopted to achieve a comprehensive evaluation: clustering accuracy (ACC), Normalized Mutual Information (NMI), Purity, Precision, Recall, Fscore, and Adjusted Rand Index (ARI). Motivated by (Nie, Cai, and Li 2017), we initialize the initial graphs G (v) by selecting 20-nearest neighbors among raw data.…”

Section: Methodsmentioning

confidence: 99%

“…Usually, different views capture different aspects of information, any of which suffices for mining knowledge (Li, Chen, and Wang 2019). Multi-view clustering, which partitions the data points into distinct clusters according to their compatible and complementary information encoded in heterogeneous features, has attracted widespread attention in the domain of unsupervised learning during the past two decades (Huang et al 2021b;Nie, Cai, and Li 2017).…”

Section: Introductionmentioning

confidence: 99%

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Multi-View Clustering on Topological Manifold

Tsang

et al. 2022

AAAI

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Multi-view clustering has received a lot of attentions in data mining recently. Though plenty of works have been investigated on this topic, it is still a severe challenge due to the complex nature of the multiple heterogeneous features. Particularly, existing multi-view clustering algorithms fail to consider the topological structure in the data, which is essential for clustering data on manifold. In this paper, we propose to exploit the implied data manifold by learning the topological relationship between data points. Our method coalesces multiple view-wise graphs with the topological relevance considered, and learns the weights as well as the consensus graph interactively in a unified framework. Furthermore, we manipulate the consensus graph by a connectivity constraint such that the data points from the same cluster are precisely connected into the same component. Substantial experiments on both toy data and real datasets are conducted to validate the effectiveness of the proposed method, compared to the state-of-the-art algorithms over the clustering performance.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-View Clustering on Topological Manifold

Tsang

et al. 2022

AAAI

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show abstract

“…Multi-view learning learns from examples with heterogeneous features, and its challenge lies in how to integrate the different feature representations in an effective way. Recently, (Nie, Cai, and Li 2017) proposes a parameter-free multi-view model, which learns the local structure among multi-view data to achieve semi-supervised classification. (Li and He 2020) proposes a bipartite graph based multiview clustering method, where a unified bipartite graph matrix is employed to fuse the consensus information across different views and directly form the final clustering results.…”

Section: Multi-view Learning (Mvl)mentioning

confidence: 99%

Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning

Lyu

Deng

et al. 2022

AAAI

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In multi-view multi-label learning (MVML), each instance is described by several heterogeneous feature representations and associated with multiple valid labels simultaneously. Although diverse MVML methods have been proposed over the last decade, most previous studies focus on leveraging the shared subspace across different views to represent the multi-view consensus information, while it is still an open issue whether such shared subspace representation is necessary when formulating the desired MVML model. In this paper, we propose a DeepGCN based View-Specific MVML method (D-VSM) which can bypass seeking for the shared subspace representation, and instead directly encoding the feature representation of each individual view through the deep GCN to couple with the information derived from the other views. Specifically, we first construct all instances under different feature representations into the corresponding feature graphs respectively, and then integrate them into a unified graph by integrating the different feature representations of each instance. Afterwards, the graph attention mechanism is adopted to aggregate and update all nodes on the unified graph to form structural representation for each instance, where both intra-view correlations and inter-view alignments have been jointly encoded to discover the underlying semantic relations. Finally, we derive a label confidence score for each instance by averaging the label confidence of its different feature representations with the multi-label soft margin loss. Extensive experiments have demonstrated that our proposed method significantly outperforms state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Reliable Multi-View Clustering

Hong

Hou

Liu

et al. 2018

AAAI

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With the advent of multi-view data, multi-view learning (MVL) has become an important research direction in machine learning. It is usually expected that multi-view algorithms can obtain better performance than that of merely using a single view. However, previous researches have pointed out that sometimes the utilization of multiple views may even deteriorate the performance. This will be a stumbling block for the practical use of MVL in real applications, especially for tasks requiring high dependability. Thus, it is eager to design reliable multi-view approaches, such that their performance is never degenerated by exploiting multiple views.This issue is vital but rarely studied. In this paper, we focus on clustering and propose the Reliable Multi-View Clustering (RMVC) method. Based on several candidate multi-view clusterings, RMVC maximizes the worst-case performance gain against the best single view clustering, which is equivalently expressed as no label information available. Specifically, employing the squared χ2 distance for clustering comparison makes the formulation of RMVC easy to solve, and an efficient strategy is proposed for optimization. Theoretically, it can be proved that the performance of RMVC will never be significantly decreased under some assumption. Experimental results on a number of data sets demonstrate that the proposed method can effectively improve the reliability of multi-view clustering.

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Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours

Cited by 316 publications

References 16 publications

Multi-View Clustering on Topological Manifold

Multi-View Clustering on Topological Manifold

Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning

Reliable Multi-View Clustering

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