Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26576
Title: A unifying framework for spectral analysis based dimensionality reduction
Authors: Zhang, T
Tao, D
Li, X
Yang, J
Keywords: Learning (artificial intelligence)
Optimisation
Spectral analysis
Artificial intelligence
Dimensionality reduction
Linear algorithms
Machine learning
Manifold learning algorithms
Patch alignment
Spectral analysis
Issue Date: 2008
Publisher: IEEE
Source: IEEE International Joint Conference on Neural Networks, 2008 : IJCNN 2008 : (IEEE World Congress on Computational Intelligence), 1-8 June 2008, Hong Kong, p. 1670-1677 How to cite?
Abstract: Past decades, numerous spectral analysis based algorithms have been proposed for dimensionality reduction, which plays an important role in machine learning and artificial intelligence. However, most of these existing algorithms are developed intuitively and pragmatically, i.e., on the base of the experience and knowledge of experts for their own purposes. Therefore, it will be more informative to provide some a systematic framework for understanding the common properties and intrinsic differences in the algorithms. In this paper, we propose such a framework, i.e., ldquopatch alignmentrdquo, which consists of two stages: part optimization and whole alignment. With the proposed framework, various algorithms including the conventional linear algorithms and the manifold learning algorithms are reformulated into a unified form, which gives us some new understandings on these algorithms.
URI: http://hdl.handle.net/10397/26576
ISBN: 978-1-4244-1820-6
978-1-4244-1821-3 (E-ISBN)
ISSN: 1098-7576
DOI: 10.1109/IJCNN.2008.4634022
Appears in Collections:Conference Paper

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