期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 31, 期 11, 页码 4857-4868出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2958324
关键词
Neural networks; Clustering methods; Task analysis; Manifolds; Deep learning; Extraterrestrial measurements; Label as representation; least square regression; low-rank representation; subspace clustering
类别
资金
- Fundamental Research Funds for the Central Universities [YJ201949]
- NFSC [61806135, 61625204, 61836006, 61836011]
- Singapore Government's Research, Innovation and Enterprise 2020 Plan (Advanced Manufacturing and Engineering Domain) [A18A1b0045]
Most popular clustering methods map raw image data into a projection space in which the clustering assignment is obtained with the vanilla k-means approach. In this article, we discovered a novel prior, namely, there exists a common invariance when assigning an image sample to clusters using different metrics. In short, different distance metrics will lead to similar soft clustering assignments on the manifold. Based on such a novel prior, we propose a novel clustering method by minimizing the discrepancy between pairwise sample assignments for each data point. To the best of our knowledge, this could be the first work to reveal the sample-assignment invariance prior based on the idea of treating labels as ideal representations. Furthermore, the proposed method is one of the first end-to-end clustering approaches, which jointly learns clustering assignment and representation. Extensive experimental results show that the proposed method is remarkably superior to 16 state-of-the-art clustering methods on five image data sets in terms of four evaluation metrics.
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