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Sparse representation of High Dimensional Data for Classification

Name: Sparse representation of High Dimensional Data for Classification
Brand: VDM Verlag
SKU: 06822011
Price: 19547 HUF
Availability: InStock
Author: Salman Siddiqui
ISBN: 9783639132991

Salman Siddiqui

Language

English

Book Paperback

Libristo code: 06822011

Publishers VDM Verlag, March 2009

In this book you will find the use of sparse §Principal Component Analysis (PCA) for representing §h... Full description

Libristo code: 06822011

134 b

19 547 Ft

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In this book you will find the use of sparse §Principal Component Analysis (PCA) for representing §high dimensional data for classification. Sparse §transformation reduces the data §volume/dimensionality without loss of critical §information, so that it can be processed efficiently §and assimilated by a human. We obtained sparse §representation of high dimensional dataset using §Sparse Principal Component Analysis (SPCA) and §Direct formulation of Sparse Principal Component §Analysis (DSPCA). Later we performed classification §using k Nearest Neighbor (kNN) Method and compared §its result with regular PCA. The experiments were §performed on hyperspectral data and various datasets §obtained from University of California, Irvine (UCI) §machine learning dataset repository. The results §suggest that sparse data representation is desirable §because sparse representation enhances §interpretation. It also improves classification §performance with certain number of features and in §most of the cases classification performance is §similar to regular PCA.