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Sparse representation of High Dimensional Data for Classification: Research and Experiments - Tapa blanda

Siddiqui, Salman

 
9783639132991: Sparse representation of High Dimensional Data for Classification: Research and Experiments
Tapa blanda
ISBN 10: 3639132998 ISBN 13: 9783639132991
Editorial: VDM Verlag Dr. Müller, 2009

Sinopsis

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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Reseña del editor

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.

Biografía del autor

Salman Siddiqui (siddiqui.m.salman@gmail.com) is a Software Engineering intern at Siemens Corporate Research. He did his MS in CS from Montclair State University. He wants to thank Dr. Peng and Dr. Robila for their help during the research. He dedicate his work to family and friends.

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Editorial
VDM Verlag Dr. Müller
Año de publicación
2009
Idioma
Inglés
ISBN 10 
3639132998
ISBN 13 
9783639132991
Encuadernación
Tapa blanda
Número de páginas
64
Contacto del fabricante
no disponible
Persona responsable
no disponible