期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 33, 期 4, 页码 1523-1539出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2940576
关键词
Encoding; Manifolds; Data models; Image reconstruction; Matrix decomposition; Computer science; Laplace equations; Unsupervised data representation; high-dimensional data clustering; robust flexible concept factorization; auto-weighting learning; robust sparse local coordinate coding
类别
资金
- National Natural Science Foundation of China [61672365, 61732008, 61725203, 61622305, 61871444, 61572339]
- High-Level Talent of Six Talent Peak Project of Jiangsu Province [XYDXX-055]
- Fundamental Research Funds for the Central Universities of China [JZ2019HGPA0102]
The study introduces a novel unsupervised Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF) framework for clustering high-dimensional data. By integrating various techniques to improve representation ability, enhance robustness to noise and errors, and optimize locality, RFA-LCF achieves state-of-the-art clustering results on public databases compared to other related methods.
Concept Factorization (CF) and its variants may produce inaccurate representation and clustering results due to the sensitivity to noise, hard constraint on the reconstruction error, and pre-obtained approximate similarities. To improve the representation ability, a novel unsupervised Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF) framework is proposed for clustering high-dimensional data. Specifically, RFA-LCF integrates the robust flexible CF by clean data space recovery, robust sparse local-coordinate coding, and adaptive weighting into a unified model. RFA-LCF improves the representations by enhancing the robustness of CF to noise and errors, providing a flexible constraint on the reconstruction error and optimizing the locality jointly. For robust learning, RFA-LCF clearly learns a sparse projection to recover the underlying clean data space, and then the flexible CF is performed in the projected feature space. RFA-LCF also uses a L-2,L-1-norm based flexible residue to encode the mismatch between the recovered data and its reconstruction, and uses the robust sparse local-coordinate coding to represent data using a few nearby basis concepts. For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates. By updating the local-coordinate preserving data, basis concepts and new coordinates alternately, the representation abilities can be potentially improved. Extensive results on public databases show that RFA-LCF delivers the state-of-the-art clustering results compared with other related methods.
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