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| Citation: |
DUAN Lijuan, GUO Yajing, XIE Chenyao, ZHANG Wenbo. Unsupervised Feature Selection Algorithm Based on Feature Clustering and Isometric Mapping[J]. Journal of Beijing University of Technology, 2024, 50(3): 325-332. DOI: 10.11936/bjutxb2022050016
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To improve the accuracy and stability of feature selection in label-free scenarios, an unsupervised feature selection algorithm based on feature clustering and isometric mapping was proposed. Feature clustering clustered features with high similarity into one class, and a new feature score measurement function was defined by combining isometric mapping and sparse coefficient matrix. This function scored the features in each feature cluster and selected the representative features with the highest scores in each class cluster to form a feature subset. Experimental results on fourteen widely used datasets show that the proposed algorithm can select features with strong classification ability and the algorithm is highly generalizable.
| [1] |
SONG J, GUO Y, GAO L, et al. From deterministic to generative: multimodal stochastic RNNs for video captioning[J]. IEEE transactions on neural networks and learning systems, 2018, 30(10): 3047-3058.
|
| [2] |
LI X, CHEN M, NIE F, et al. A multiview-based parameter free framework for group detection[C]//Thirty-first AAAI Conference on Artificial Intelligence. Palo Alto, California, USA: Association for the Advancement of Artificial Intelligence, 2017: 4147-4153.
|
| [3] |
LI X, CHEN M, NIE F, et al. Locality adaptive discriminant analysis[C]//Proceedings of the Twenty-sixth International Joint Conference on Artificial Intelligence. Freiburg, Germany: IJCAI, 2017: 2201-2207.
|
| [4] |
ZHANG R, TONG H. Robust principal component analysis with adaptive neighbors[J]. Advances in neural information processing systems, 2019, 32(8): 6959-6967.
|
| [5] |
WANG F, WANG Q, NIE F, et al. Unsupervised linear discriminant analysis for jointly clustering and subspace learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 33(3): 1276-1290.
|
| [6] |
SONG J, GAO L, NIE F, et al. Optimized graph learning using partial tags and multiple features for image and video annotation[J]. IEEE Transactions on Image Processing, 2016, 25(11): 4999-5011.
|
| [7] |
LEE P Y, LOH W P, CHIN J F. Feature selection in multimedia: the state-of-the-art review[J]. Image and Vision Computing, 2017, 67: 29-42.
|
| [8] |
ZHANG R, NIE F, LI X, et al. Feature selection with multi-view data: a survey[J]. Information Fusion, 2019, 50: 158-167.
|
| [9] |
HE X, CAI D, NIYOGI P. Laplacian score for feature selection[J]. Advances in Neural Information Processing Systems, 2005, 18(5): 504-514.
|
| [10] |
DU S, MA Y, LI S, et al. Robust unsupervised feature selection via matrix factorization[J]. Neurocomputing, 2017, 241: 115-127.
|
| [11] |
TANG C, LIU X, LI M, et al. Robust unsupervised feature selection via dual self-representation and manifold regularization[J]. Knowledge-Based Systems, 2018, 145: 109-120.
|
| [12] |
GUO J, GUO Y, KONG X, et al. Unsupervised feature selection with ordinal locality[C]//2017 IEEE International Conference on Multimedia and Expo (ICME). Piscataway, NJ: IEEE, 2017: 1213-1218.
|
| [13] |
WANG S, TANG J, LIU H. Embedded unsupervised feature selection[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, California: Association for the Advancement of Artificial Intelligence, 2015: 470-476.
|
| [14] |
GUO J, ZHU W. Dependence guided unsupervised feature selection[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, California: Association for the Advancement of Artificial Intelligence, 2018: 2232-2239.
|
| [15] |
WANG X, ZHANG X, ZENG Z, et al. Unsupervised spectral feature selection with l1-norm graph[J]. Neurocomputing, 2016, 200: 47-54.
|
| [16] |
PENG C, KANG Z, YANG M, et al. Feature selection embedded subspace clustering[J]. IEEE Signal Processing Letters, 2016, 23(7): 1018-1022.
|
| [17] |
LI Z, YANG Y, LIU J, et al. Unsupervised feature selection using nonnegative spectral analysis[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, California: Association for the Advancement of Artificial Intelligence 2012: 1026-1032.
|
| [18] |
YANG Y, SHEN H T, MA Z, et al. L2, 1-norm regularized discriminative feature selection for unsupervised[C]//Proceedings of the Twenty-second International Joint Conference on Artificial Intelligence. Freiburg, Germany: IJCAI, 2011: 1589-1594.
|
| [19] |
CAI D, ZHANG C, HE X. Unsupervised feature selection for multi-cluster data[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery, 2010: 333-342.
|
| [20] |
谢娟英, 丁丽娟, 王明钊. 基于谱聚类的无监督特征选择算法[J]. 软件学报, 2020, 31(4): 1009-1024.
XIE J Y, DING L J, WANG M Z. Unsupervised feature selection algorithm based on spectral clustering[J] Journal of Software, 2020, 31(4): 1009-1024. (in Chinese)
|
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关金金. 基于协同演化遗传算法的个体特征信息识别技术方法. 长沙大学学报. 2024(05): 31-38 .
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Supported by: Beijing Renhe Information Technology Co., Ltd. 
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