Könyv Sparse representation of High Dimensional Data for Classification Salman Siddiqui

Sparse representation of High Dimensional Data for Classification

Szerző: Salman Siddiqui
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: VDM Verlag
Elérhetőség: Beszállítói készleten
Küldés 14-21 napon belül
19 598 Ft
In this book you will find the use of sparse §Principal Component Analysis (PCA) for representing §h...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2009
oldal
64
EAN
9783639132991
ISBN
3639132998
Enbook ID
06822011
Kiadó
Súly
104
Méretek
152 x 229 x 4

Teljes leírás

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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