Unsupervised Dimensionality Reduction: Overview and Recent Advances
(2010) International Joint Conference on Neural Networks (IJCNN 2010) — Location: Barcelona (Spain) (18.July.2010)
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- Abstract
- Unsupervised dimensionality reduction aims at representing high-dimensional data in lower-dimensional spaces in a faithful way. Dimensionality reduction can be used for compression or denoising purposes, but data visualization remains one its most prominent applications. This paper attempts to give a broad overview of the domain. Past developments are briefly introduced and pinned up on the time line of the last eleven decades. Next, the principles and techniques involved in the major methods are described. A taxonomy of the methods is suggested, taking into account various properties. Finally, the issue of quality assessment is briefly dealt with.
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Lee, J., & Verleysen, M. (2010). Unsupervised Dimensionality Reduction: Overview and Recent Advances. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2010), p. 4163-4170. https://hdl.handle.net/2078.5/253742