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- W4385314393 abstract "This chapter focuses on the combination of Non-Negative Matrix Factorization (NMF) and manifold learning for multi-aspect data clustering. Manifold learning is an established method for dimensionality reduction that preserves the geometric structure of the data while mapping it to a lower-dimensional space. This chapter begins with a comprehensive introduction to manifold learning and its application in conventional single-aspect data and equips readers with a solid understanding of the fundamental concepts necessary for the subsequent discussion on manifold learning in multi-aspect data clustering. The chapter then discusses the challenges of applying manifold learning to NMF-based clustering on multi-aspect data and reviews the different approaches proposed to address this problem. It provides a comprehensive discussion of significant research gaps in this field and potential areas for future work." @default.
- W4385314393 created "2023-07-28" @default.
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- W4385314393 date "2023-01-01" @default.
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- W4385314393 title "NMF and Manifold Learning for Multi-aspect Data" @default.
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- W4385314393 doi "https://doi.org/10.1007/978-3-031-33560-0_3" @default.
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