Subspace clustering with automatic feature grouping

Gan, Guojun; Ng, Michael K.

doi:10.1016/j.patcog.2015.05.016

Cited by 36 publications

(12 citation statements)

References 24 publications

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“…Many SSC algorithms have been proposed to cluster highdimensional data and to find the important subspace for each group [44][45][46]. Unlike most existing SSC algorithms that only 1063-6706 (c) 2018 IEEE.…”

Section: A Antecedent Parameter Estimation Using Esscmentioning

confidence: 99%

Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

Deng

Zhang

et al. 2019

IEEE Trans. Fuzzy Syst.

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Section: A Antecedent Parameter Estimation Using Esscmentioning

confidence: 99%

Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

Deng

Zhang

et al. 2019

IEEE Trans. Fuzzy Syst.

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“…As traditional SSC algorithms are not suitable for multi-view data, multi-view learning has been proposed [12,16,30] and is becoming a popular approach in the field of machine learning.…”

Section: Multi-view Learningmentioning

confidence: 99%

“…In addition to the seven broad categories of XSSC algorithms discussed above, many other XSSC algorithms have recently been reported [4,6,8,15,27,30,31,48,63,67]. A brief summary is given below.…”

Section: Other Xssc Algorithmsmentioning

confidence: 99%

A survey on soft subspace clustering

Deng

Choi

Jiang

et al. 2016

Information Sciences

101

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Subspace clustering (SC) is a promising technology involving clusters that are identified based on their association with subspaces in high-dimensional spaces. SC can be classified into hard subspace clustering (HSC) and soft subspace clustering (SSC). While HSC algorithms have been studied extensively and are well accepted by the scientific community, SSC algorithms are relatively new. However, as they are said to be more adaptable than their HSC counterparts, SSC algorithms have been attracting more attention in recent years. A comprehensive survey of existing SSC algorithms and recent developments in the field are presented in this paper. SSC algorithms have been systematically classified into three main categories: conventional SSC (CSSC), independent SSC (ISSC), and extended SSC (XSSC). The characteristics of these algorithms are highlighted and potential future developments in the area of SSC are discussed. Through a comprehensive review of SSC, this paper aims to provide readers with a clear profile of existing SSC methods and to foster the development of more effective clustering technologies and significant research in this area.As discussed previously, SSC algorithms can be broadly classified into three main categories: CSSC, ISSC, and XSSC. Each of these categories can be further divided into subcategories based on the clustering mechanisms that are adopted, as shown in Table 3. In CSSC, clustering is performed by first identifying the subspace using some strategies, and then carrying out clustering in the subspace that was obtained, in order to partition the data. This is referred to as separated feature weighting, where data partitioning involves two separate processessubspace identification and clustering in subspace. Clustering can also be conducted by performing the two processes simultaneously, an approach known as coupled feature weighting. In ISSC, algorithms are developed based on the K-means model, fuzzy C-means (FCM) model, and probability mixture model, in a process where fuzzy weighting, entropy weighting, or other weighting mechanisms are adopted to implement feature weighting. Finally, XSSC algorithms can be subdivided into eight subcategories, depending on the strategies used to enhance the CSSC and ISSC algorithms. These subcategories are between-class separation, evolutionary learning, the adoption of new metrics, ensemble learning, multi-view learning, imbalanced * Denotes the values achieved by each algorithm when the lowest value of the loss function is obtained within the 10 runs.

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“…From the above example we see that choosing an appropriate value for the parameter γ is a difficult task when the attribute dispersions in a cluster is large. Feature group weighting has been introduced to address the issue [7,14].…”

Section: Guojun Gan and Kun Chenmentioning

confidence: 99%

A soft subspace clustering algorithm with log-transformed distances

Gan¹,

Chen

2015

BDIA

Self Cite

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Entropy weighting used in some soft subspace clustering algorithms is sensitive to the scaling parameter. In this paper, we propose a novel soft subspace clustering algorithm by using log-transformed distances in the objective function. The proposed algorithm allows users to choose a value of the scaling parameter easily because the entropy weighting in the proposed algorithm is less sensitive to the scaling parameter. In addition, the proposed algorithm is less sensitive to noises because a point far away from its cluster center is given a small weight in the cluster center calculation. Experiments on both synthetic datasets and real datasets are used to demonstrate the performance of the proposed algorithm.

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Subspace clustering with automatic feature grouping

Cited by 36 publications

References 24 publications

Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

A survey on soft subspace clustering

A soft subspace clustering algorithm with log-transformed distances

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