Sparse representation of High Dimensional Data for Classification

Name: Sparse representation of High Dimensional Data for Classification
Brand: VDM Verlag
SKU: 9783639132991
Price: 49.98 EUR
Availability: InStock

Автор: Salman Siddiqui

Език:

Корици: С меки корици

Издател: VDM Verlag

Наличност: Външен склад

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49.98 € 97.74 лв

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

Информация за книгата

Автор

Salman Siddiqui

Език

Английски език

Корици

Книга - С меки корици

Издадена

2009

страници

EAN

9783639132991

ISBN

3639132998

Enbook ID

06822011

Издател

VDM Verlag

Теглоt

104

Размери

152 x 229 x 4

Пълно описание

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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