Sparse representation of High Dimensional Data for Classification

Name: Sparse representation of High Dimensional Data for Classification
Brand: VDM Verlag
SKU: 9783639132991
Price: 1149 CZK
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Autor: Salman Siddiqui

Jazyk:

Vazba: Brožovaná

Vydavatel: VDM Verlag

Dostupnost: Skladem u dodavatele

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

Informace o knize

Autor

Salman Siddiqui

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2009

Stránek

EAN

9783639132991

ISBN

3639132998

Enbook ID

06822011

Vydavatel

VDM Verlag

Hmotnost

104

Rozměry

152 x 229 x 4

Kompletní popis

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.

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Sparse representation of High Dimensional Data for Classification

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