Sparse representation of High Dimensional Data for Classification : Research and Experiments
Language: English
Published by VDM Verlag Dr. Müller, 2009
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…
Seller Inventory # 9783639132991
- Title
- Sparse representation of High Dimensional Data for Classification : Research and Experiments
- Author
- Salman Siddiqui
- Publisher
- VDM Verlag Dr. Müller
- Publication year
- 2009
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3639132998
- ISBN 13
- 9783639132991
- Item weight
- 103 grams
- Dimensions
- 220x150x4 mm
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About the Author
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