Non-Negative Local Sparse Coding for Subspace Clustering

Hosseini B, Hammer B (2018)
Advances in Intelligent Data Analysis XVII. IDA 2018.

Preprint | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Subspace sparse coding (SSC) algorithms have proven to be beneficial to the clustering problems. They provide an alternative data representation in which the underlying structure of the clusters can be better captured. However, most of the research in this area is mainly focused on enhancing the sparse coding part of the problem. In contrast, we introduce a novel objective term in our proposed SSC framework which focuses on the separability of data points in the coding space. We also provide mathematical insights into how this local-separability term can improve the clustering result of the SSC framework. Our proposed non-linear local SSC algorithm (NLSSC) also benefits from the efficient choice of its sparsity terms and constraints. The NLSSC algorithm is also formulated in the kernel-based framework (NLKSSC) which can represent the nonlinear structure of data. In addition, we address the possibility of having redundancies in sparse coding results and its negative effect on graph-based clustering problems. Accordingly, we introduce the link-restore post-processing step to improve the representation graph of non-negative SSC algorithms such as ours. Empirical evaluations on well-known clustering benchmarks show that our proposed NLSSC framework results in better clusterings compared to the state-of-the-art baselines, and demonstrate the effectiveness of the link-restore post-processing in improving the clustering accuracy via correcting the broken links of the representation graph.
Stichworte
Machine Learning; Data Mining; Subspace Clustering; Sparse Coding
Erscheinungsjahr
2018
Zeitschriftentitel
Advances in Intelligent Data Analysis XVII. IDA 2018
ISBN
978-3-030-01767-5
Page URI
https://pub.uni-bielefeld.de/record/2921209

Zitieren

Hosseini B, Hammer B. Non-Negative Local Sparse Coding for Subspace Clustering. Advances in Intelligent Data Analysis XVII. IDA 2018. 2018.
Hosseini, B., & Hammer, B. (2018). Non-Negative Local Sparse Coding for Subspace Clustering. Advances in Intelligent Data Analysis XVII. IDA 2018
Hosseini, B., and Hammer, B. (2018). Non-Negative Local Sparse Coding for Subspace Clustering. Advances in Intelligent Data Analysis XVII. IDA 2018.
Hosseini, B., & Hammer, B., 2018. Non-Negative Local Sparse Coding for Subspace Clustering. Advances in Intelligent Data Analysis XVII. IDA 2018.
B. Hosseini and B. Hammer, “Non-Negative Local Sparse Coding for Subspace Clustering”, Advances in Intelligent Data Analysis XVII. IDA 2018, 2018.
Hosseini, B., Hammer, B.: Non-Negative Local Sparse Coding for Subspace Clustering. Advances in Intelligent Data Analysis XVII. IDA 2018. (2018).
Hosseini, Babak, and Hammer, Barbara. “Non-Negative Local Sparse Coding for Subspace Clustering”. Advances in Intelligent Data Analysis XVII. IDA 2018 (2018).
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arXiv: 1903.05239

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