Research on Dimensionality Reduction Based on Neighborhood Preserving Embedding and Sparse Representation

Abstract:

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In real world, high-dimensional data are everywhere, but the nature structure behind them is always featured by only a few parameters. With the rapid development of computer vision, more and more data dimensionality reduction problems are involved, this leads to the rapid development of dimensionality reduction algorithms. Linear method such as LPP [1], NPE [2], nonlinear method such as LLE [3] and improvement version kernel NPE. One particularly simple but effective assumption in face recognition is that the samples from the same class lie on a linear subspace, so lots of nonlinear methods only perform well on some artificial data sets. This paper emphasizes on NPE and SPP [4] come up with recently, and combines these methods, the experiments show the effect of new method outperform some classic unsupervised methods.

Info:

Periodical:

Edited by:

Qi Luo

Pages:

547-550

DOI:

10.4028/www.scientific.net/AMM.58-60.547

Citation:

D. Wu and Z. Zheng, "Research on Dimensionality Reduction Based on Neighborhood Preserving Embedding and Sparse Representation", Applied Mechanics and Materials, Vols. 58-60, pp. 547-550, 2011

Online since:

June 2011

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$35.00

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