Książka Sparse representation of High Dimensional Data for Classification Salman Siddiqui

Sparse representation of High Dimensional Data for Classification

Język: Angielski
Oprawa: Miękka
Wydawca: VDM Verlag
Dostępność: Dostępna u dostawcy
Wysyłamy za 9-15 dni
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In this book you will find the use of sparse §Principal Component Analysis (PCA) for representing §h...

Informacje o książce

Język
Angielski
Oprawa
Książka - Miękka
Data wydania
2009
strony
64
EAN
9783639132991
ISBN
3639132998
Enbook ID
06822011
Wydawca
Waga
104
Wymiary
152 x 229 x 4

Pełny opis

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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