Sparse Clustering Algorithm Based on Multi-Domain Dimensionality Reduction Autoencoder

The key to high-dimensional clustering lies in discovering the intrinsic structures and patterns in data to provide valuable information. However, high-dimensional clustering faces enormous challenges such as dimensionality disaster, increased data sparsity, and reduced reliability of the clustering...

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Published inMathematics (Basel) Vol. 12; no. 10; p. 1526
Main Authors Kang, Yu, Liu, Erwei, Zou, Kaichi, Wang, Xiuyun, Zhang, Huaqing
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2024
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Abstract The key to high-dimensional clustering lies in discovering the intrinsic structures and patterns in data to provide valuable information. However, high-dimensional clustering faces enormous challenges such as dimensionality disaster, increased data sparsity, and reduced reliability of the clustering results. In order to address these issues, we propose a sparse clustering algorithm based on a multi-domain dimensionality reduction model. This method achieves high-dimensional clustering by integrating the sparse reconstruction process and sparse L1 regularization into a deep autoencoder model. A sparse reconstruction module is designed based on the L1 sparse reconstruction of features under different domains to reconstruct the data. The proposed method mainly contributes in two aspects. Firstly, the spatial and frequency domains are combined by taking into account the spatial distribution and frequency characteristics of the data to provide multiple perspectives and choices for data analysis and processing. Then, a neural network-based clustering model with sparsity is conducted by projecting data points onto multi-domains and implementing adaptive regularization penalty terms to the weight matrix. The experimental results demonstrate superior performance of the proposed method in handling clustering problems on high-dimensional datasets.
AbstractList The key to high-dimensional clustering lies in discovering the intrinsic structures and patterns in data to provide valuable information. However, high-dimensional clustering faces enormous challenges such as dimensionality disaster, increased data sparsity, and reduced reliability of the clustering results. In order to address these issues, we propose a sparse clustering algorithm based on a multi-domain dimensionality reduction model. This method achieves high-dimensional clustering by integrating the sparse reconstruction process and sparse L1 regularization into a deep autoencoder model. A sparse reconstruction module is designed based on the L1 sparse reconstruction of features under different domains to reconstruct the data. The proposed method mainly contributes in two aspects. Firstly, the spatial and frequency domains are combined by taking into account the spatial distribution and frequency characteristics of the data to provide multiple perspectives and choices for data analysis and processing. Then, a neural network-based clustering model with sparsity is conducted by projecting data points onto multi-domains and implementing adaptive regularization penalty terms to the weight matrix. The experimental results demonstrate superior performance of the proposed method in handling clustering problems on high-dimensional datasets.
Audience Academic
Author Zou, Kaichi
Liu, Erwei
Zhang, Huaqing
Kang, Yu
Wang, Xiuyun
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Cites_doi 10.1007/978-3-642-33786-4_26
10.1109/TKDE.2004.25
10.1109/TKDE.2023.3266451
10.1109/8.841899
10.1109/TKDE.2018.2842191
10.1109/TNNLS.2021.3097748
10.1109/TNNLS.2021.3105822
10.1109/TPAMI.2012.88
10.3390/math11173785
10.1109/TPAMI.2022.3216454
10.1109/TCYB.2021.3049633
10.1109/TNNLS.2022.3185638
10.1109/TNNLS.2021.3085891
10.1109/TKDE.2005.75
10.1109/ICCV.2015.123
10.1109/TKDE.2007.1048
10.3390/math12030453
10.1109/TNNLS.2015.2490080
10.1109/TNNLS.2017.2728138
10.1109/TKDE.2015.2460735
10.3390/math12040508
10.1109/TNNLS.2022.3151498
10.1109/TNNLS.2014.2337335
10.1109/TNNLS.2021.3071275
10.1109/TNNLS.2020.3029033
10.1109/TKDE.2003.1198398
10.1109/TNNLS.2020.2978389
10.1109/TKDE.2020.3028943
10.1109/TKDE.2022.3193569
10.1109/TCYB.2014.2358564
10.1109/TPAMI.2018.2889949
10.1109/TNNLS.2021.3135460
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References Wu (ref_27) 2023; 34
ref_33
Jia (ref_5) 2018; 29
ref_32
Wang (ref_22) 2023; 35
Ordonez (ref_11) 2004; 16
Xu (ref_16) 2023; 35
Ji (ref_24) 2022; 33
(ref_30) 2005; 17
Hou (ref_6) 2015; 26
Li (ref_19) 2024; 35
Jing (ref_8) 2007; 19
Yang (ref_15) 2023; 34
ref_39
Li (ref_25) 2021; 32
ref_38
Ng (ref_36) 2001; 14
Liu (ref_37) 2013; 35
Guan (ref_13) 2023; 35
Zhao (ref_14) 2023; 35
Cai (ref_35) 2015; 45
Yang (ref_26) 2023; 34
Peng (ref_34) 2016; 27
Peng (ref_7) 2018; 23
Castelli (ref_9) 2003; 15
Zhao (ref_17) 2023; 34
Almalawi (ref_10) 2016; 28
Werner (ref_31) 2000; 48
Guan (ref_18) 2022; 34
ref_1
Chang (ref_23) 2020; 42
ref_3
ref_2
Huang (ref_20) 2023; 45
Wang (ref_29) 2022; 44
Wu (ref_28) 2022; 33
Rathore (ref_12) 2019; 31
Wang (ref_21) 2022; 33
Huang (ref_4) 2022; 52
References_xml – ident: ref_38
  doi: 10.1007/978-3-642-33786-4_26
– volume: 16
  start-page: 909
  year: 2004
  ident: ref_11
  article-title: Efficient disk-based k-means clustering for relational databases
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2004.25
  contributor:
    fullname: Ordonez
– volume: 35
  start-page: 10814
  year: 2023
  ident: ref_13
  article-title: DEMOS: Clustering by pruning a density-boosting cluster tree of density mounts
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2023.3266451
  contributor:
    fullname: Guan
– volume: 48
  start-page: 383
  year: 2000
  ident: ref_31
  article-title: The simultaneous interpolation of antenna radiation patterns in both the spatial and frequency domains using model-based parameter estimation
  publication-title: IEEE Trans. Antennas Propag.
  doi: 10.1109/8.841899
  contributor:
    fullname: Werner
– volume: 31
  start-page: 641
  year: 2019
  ident: ref_12
  article-title: A rapid hybrid clustering algorithm for large volumes of high dimensional data
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2018.2842191
  contributor:
    fullname: Rathore
– volume: 34
  start-page: 516
  year: 2023
  ident: ref_15
  article-title: Deep multiview collaborative clustering
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2021.3097748
  contributor:
    fullname: Yang
– volume: 34
  start-page: 2068
  year: 2023
  ident: ref_17
  article-title: Spectral clustering with adaptive neighbors for deep learning
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2021.3105822
  contributor:
    fullname: Zhao
– volume: 14
  start-page: 849
  year: 2001
  ident: ref_36
  article-title: On spectral clustering: Analysis and an algorithm
  publication-title: Adv. Neural Inf. Process. Syst.
  contributor:
    fullname: Ng
– volume: 35
  start-page: 171
  year: 2013
  ident: ref_37
  article-title: Robust recovery of subspace structures by low-rank representation
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  doi: 10.1109/TPAMI.2012.88
  contributor:
    fullname: Liu
– ident: ref_1
  doi: 10.3390/math11173785
– volume: 45
  start-page: 7509
  year: 2023
  ident: ref_20
  article-title: Learning representation for clustering via prototype scattering and positive sampling
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  doi: 10.1109/TPAMI.2022.3216454
  contributor:
    fullname: Huang
– volume: 52
  start-page: 12231
  year: 2022
  ident: ref_4
  article-title: Toward multidiversified ensemble clustering of high-dimensional data: From subspaces to metrics and beyond
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2021.3049633
  contributor:
    fullname: Huang
– volume: 35
  start-page: 1857
  year: 2024
  ident: ref_19
  article-title: Self-supervised self-organizing clustering network: A novel unsupervised representation learning method
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2022.3185638
  contributor:
    fullname: Li
– volume: 33
  start-page: 7610
  year: 2022
  ident: ref_21
  article-title: DNB: A joint learning framework for deep bayesian nonparametric clustering
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2021.3085891
  contributor:
    fullname: Wang
– ident: ref_39
– volume: 17
  start-page: 628
  year: 2005
  ident: ref_30
  article-title: Dual clustering: Integrating data clustering over optimization and constraint domains
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2005.75
– ident: ref_32
  doi: 10.1109/ICCV.2015.123
– volume: 19
  start-page: 1026
  year: 2007
  ident: ref_8
  article-title: An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2007.1048
  contributor:
    fullname: Jing
– ident: ref_2
  doi: 10.3390/math12030453
– volume: 23
  start-page: 227
  year: 2018
  ident: ref_7
  article-title: XAI beyond classification: Interpretable neural clustering
  publication-title: J. Mach. Learn. Res.
  contributor:
    fullname: Peng
– volume: 27
  start-page: 2499
  year: 2016
  ident: ref_34
  article-title: A unified framework for representation-based subspace clustering of out-of-sample and large-scale data
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2015.2490080
  contributor:
    fullname: Peng
– volume: 35
  start-page: 3001
  year: 2023
  ident: ref_14
  article-title: Robust fuzzy k-means clustering with shrunk patterns learning
  publication-title: IEEE Trans. Knowl. Data Eng.
  contributor:
    fullname: Zhao
– volume: 29
  start-page: 3308
  year: 2018
  ident: ref_5
  article-title: Subspace clustering of categorical and numerical data with an unknown number of clusters
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2017.2728138
  contributor:
    fullname: Jia
– volume: 28
  start-page: 68
  year: 2016
  ident: ref_10
  article-title: k NNVWC: An efficient k -nearest neighbors approach based on various-widths clustering
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2015.2460735
  contributor:
    fullname: Almalawi
– ident: ref_33
– ident: ref_3
  doi: 10.3390/math12040508
– volume: 34
  start-page: 8543
  year: 2023
  ident: ref_27
  article-title: Deep clustering and visualization for end-to-end high-dimensional data analysis
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2022.3151498
  contributor:
    fullname: Wu
– volume: 35
  start-page: 5035
  year: 2023
  ident: ref_22
  article-title: Local-to-global deep clustering on approximate uniform manifold
  publication-title: IEEE Trans. Knowl. Data Eng.
  contributor:
    fullname: Wang
– volume: 44
  start-page: 5042
  year: 2022
  ident: ref_29
  article-title: Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  contributor:
    fullname: Wang
– volume: 26
  start-page: 1287
  year: 2015
  ident: ref_6
  article-title: Discriminative embedded clustering: A framework for grouping high-dimensional data
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2014.2337335
  contributor:
    fullname: Hou
– volume: 33
  start-page: 5681
  year: 2022
  ident: ref_24
  article-title: A decoder-free variational deep embedding for unsupervised clustering
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2021.3071275
  contributor:
    fullname: Ji
– volume: 33
  start-page: 774
  year: 2022
  ident: ref_28
  article-title: Semisupervised feature learning by deep entropy-sparsity subspace clustering
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2020.3029033
  contributor:
    fullname: Wu
– volume: 15
  start-page: 671
  year: 2003
  ident: ref_9
  article-title: CSVD: Clustering and singular value decomposition for approximate similarity search in high-dimensional spaces
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2003.1198398
  contributor:
    fullname: Castelli
– volume: 32
  start-page: 443
  year: 2021
  ident: ref_25
  article-title: Autoencoder constrained clustering with adaptive neighbors
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2020.2978389
  contributor:
    fullname: Li
– volume: 34
  start-page: 3669
  year: 2022
  ident: ref_18
  article-title: Deep feature-based text clustering and its explanation
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2020.3028943
  contributor:
    fullname: Guan
– volume: 35
  start-page: 7470
  year: 2023
  ident: ref_16
  article-title: Self-supervised discriminative feature learning for deep multi-view clustering
  publication-title: IEEE Trans. Knowl. Data Eng.
  doi: 10.1109/TKDE.2022.3193569
  contributor:
    fullname: Xu
– volume: 45
  start-page: 1669
  year: 2015
  ident: ref_35
  article-title: Large scale spectral clustering via landmark-based sparse representation
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2014.2358564
  contributor:
    fullname: Cai
– volume: 42
  start-page: 809
  year: 2020
  ident: ref_23
  article-title: Deep self-evolution clustering
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  doi: 10.1109/TPAMI.2018.2889949
  contributor:
    fullname: Chang
– volume: 34
  start-page: 6303
  year: 2023
  ident: ref_26
  article-title: Deep clustering analysis via dual variational autoencoder with spherical latent embeddings
  publication-title: IEEE Trans. Neural Netw. Learn. Syst.
  doi: 10.1109/TNNLS.2021.3135460
  contributor:
    fullname: Yang
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Snippet The key to high-dimensional clustering lies in discovering the intrinsic structures and patterns in data to provide valuable information. However,...
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SubjectTerms Algorithms
Analysis
Clustering
Data analysis
Data points
Decomposition
Deep learning
high dimensional
Information management
Methods
multi-domain
Neural networks
Reconstruction
Regularization
Sparsity
Spatial distribution
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Title Sparse Clustering Algorithm Based on Multi-Domain Dimensionality Reduction Autoencoder
URI https://www.proquest.com/docview/3059603850
https://doaj.org/article/51dba8ca2a5b4d8ebcb12b27584735db
Volume 12
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