Sparse representation of High Dimensional Data for Classification

ISBN-13: 9783639132991 / Angielski / Miękka / 2009 / 64 str.

Salman Siddiqui

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.

O autorze

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.