[1]CHU Derun,ZHOU Zhiping.An autoencoder spectral clustering algorithm for improving landmark representation by weighted PageRank[J].CAAI Transactions on Intelligent Systems,2020,15(2):302-309.[doi:10.11992/tis.201904021]
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An autoencoder spectral clustering algorithm for improving landmark representation by weighted PageRank
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 2
Page number:
302-309
Column:
学术论文—机器学习
Public date:
2020-03-05
- Title:
- An autoencoder spectral clustering algorithm for improving landmark representation by weighted PageRank
- Keywords:
- machine learning; data mining; cluster analysis; landmark spectral clustering; spectral clustering; weighted pagerank; autoencoder; clustering loss
- CLC:
- TP18
- DOI:
- 10.11992/tis.201904021
- Abstract:
- Several problems, such as low clustering precision, large memory overhead of the similarity matrix, and high computational complexity of the Laplace matrix eigenvalue decomposition, are encountered when using the traditional spectral clustering algorithm to deal with large-scale datasets. To solve these problems, an autoencoder spectral clustering algorithm for improving landmark representation by weighted PageRank is proposed in this study. First, the nodes with the highest weight in the data affinity graph were selected as the landmark points. The similarity matrix was approximated by the similarity relation between the selected ground punctuation points and other data points. The result was further used as the input of the superimposed automatic encoder. At the same time, the parameters of the automatic encoder and cluster center were updated simultaneously using clustering loss. Thus, extensible and accurate clustering can be achieved. The experimental results show that the proposed autoencoder spectral clustering algorithm has better clustering performance than the landmark and depth spectral clustering algorithms on several typical datasets.
- References:
-
[1] LI Mu, BI Wei, KWOK J T, et al. Large-scale nystr?m kernel matrix approximation using randomized SVD[J]. IEEE transactions on neural networks and learning systems, 2015, 26(1): 152-164.
[2] 赵晓晓, 周治平. 结合稀疏表示与约束传递的半监督谱聚类算法[J]. 智能系统学报, 2018, 13(5): 855-863
ZHAO Xiaoxiao, ZHOU Zhiping. A semi-supervised spectral clustering algorithm combined with sparse representation and constraint propagation[J]. CAAI transactions on intelligent systems, 2018, 13(5): 855-863
[3] DING Shifei, JIA Hongjie, DU Mingjing, et al. A semi-supervised approximate spectral clustering algorithm based on HMRF model[J]. Information sciences, 2018, 429: 215-228.
[4] HE Li, RAY N, GUAN Yisheng, et al. Fast large-scale spectral clustering via explicit feature mapping[J]. IEEE transactions on cybernetics, 2019, 49(3): 1058-1071.
[5] 林大华, 杨利锋, 邓振云, 等. 稀疏样本自表达子空间聚类算法[J]. 智能系统学报, 2016, 11(5): 696-702
LIN Dahua, YANG Lifeng, DENG Zhenyun, et al. Sparse sample self-representation for subspace clustering[J]. CAAI transactions on intelligent systems, 2016, 11(5): 696-702
[6] YANG Xiaojun, YU Weizhong, WANG Rong, et al. Fast spectral clustering learning with hierarchical bipartite graph for large-scale data[J/OL]. Pattern recognition letters: (2018-06-22). https://www.sciencedirect.com/science/article/abs/pii/S016786551830271X. DOI: 10.1016/J.PATREC.2018.06.024.
[7] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[8] SHAHAM U, STANTON K, LI H, et al. SpectralNet: spectral clustering using deep neural networks[J]. arXiv:1801.01587, 2018.
[9] TIAN Fei, GAO Bin, CUI Qing, et al. Learning deep representations for graph clustering[C]//Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. Québec City, Canada, 2014: 1293?1299.
[10] SHAO Ming, LI Sheng, DING Zhengming, et al. Deep linear coding for fast graph clustering[C]//Proceedings of the 24th International Conference on Artificial Intelligence. Buenos Aires, Argentina, 2015: 3798?3804.
[11] SONG Chunfeng, LIU Feng, HUANG Yongzhen, et al. Auto-encoder based data clustering[C]//Proceedings of the 18th Iberoamerican Congress on Pattern Recognition. Havana, Cuba, 2013: 117?124.
[12] PAGE L, BRIN S, MOTWANI R, et al. The PageRank citation ranking: bringing order to the web. SIDL?WP?1999?0120[R]. Technical Report, California: Stanford Digital Libraries, 1999.
[13] XING W, GHORBANI A. Weighted PageRank algorithm[C]//Proceedings of Second Annual Conference on Communication Networks and Services Research. Fredericton, Canada, 2004: 305?314.
[14] CHEN Xinlei, CAI Deng. Large scale spectral clustering with landmark-based representation[C]//Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. San Francisco, USA, 2011: 313?318.
[15] CAI Deng, CHEN Xinlei. Large scale spectral clustering via landmark-based sparse representation[J]. IEEE transactions on cybernetics, 2015, 45(8): 1669-1680.
[16] BENGIO Y, COURVILLE A, VINCENT P. Representation learning: a review and new perspectives[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(8): 1798-1828.
[17] XIE Junyuan, GIRSHICK R B, FARHADI A. Unsupervised deep embedding for clustering analysis[C]//Proceedings of the 33rd International Conference on Machine Learning. New York, USA, 2016: 478?487.
[18] LI Mu, ZHANG Tong, CHEN Yuqiang, et al. Efficient mini-batch training for stochastic optimization[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2014: 661?670.
[19] LI Mu, KWOK J T, LU Baoliang. Making large-scale Nystr?m approximation possible[C]//Proceedings of the 27th International Conference on Machine Learning. Haifa, Israel, 2010: 631?638.
[20] CHEN W Y, SONG Yangqiu, Bai Hongjie, et al. Parallel spectral clustering in distributed systems[J]. IEEE transactions on pattern analysis and machine intelligence, 2011, 33(3): 568-586.
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