NDLI: An Investigation on Recent Advances in Feature Partitioning Based Principal Component Analysis Methods

Content Provider	IEEE Xplore Digital Library
Author	Negi, A. Kadappa, V.
Copyright Year	2010
Abstract	Principal Component Analysis (PCA) is one of the well-known and widely accepted dimensionality reduction techniques in varied domains. However, PCA does not scale well computationally with increasing dimensionality and it extracts only global features, ignoring local features. The local features may be very useful for classification. More recently, partitioning based PCA approaches (FP-PCA) have been proposed to compute principal components to overcome these shortcomings. In this paper we analyze the existing FPPCA methods and classify them into meaningful categories for better understanding. Subsequently we bring out the properties of these FP-PCA methods. This analysis provides the basis for further research on the FP-PCA approaches which appear to promise improvements in classification as well as savings in computation. We also show the superiority of these recent FPPCA methods using our experimentation on ORL and Yale face data sets.
Starting Page	90
Ending Page	95
File Size	529365
Page Count	6
File Format	PDF
ISBN	9781424496280
DOI	10.1109/VCON.2010.26
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-09
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dimensionality reduction Correlation Face recognition Feature extraction Feature partitioning Face Computational complexity Principal component analysis PCA
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

An Empirical Study of Dimensionality Reduction Methods for Biometric Recognition

IRIS recognition based on PCA based Dimensionality reduction and SVM

An improved feature extraction method based on DWT and 2DSubXPCA methods

Dimensionality reduction based on Lorentzian Manifold for face recognition

A theoretical investigation of feature partitioning principal component analysis methods

Optimizing modular image PCA using Genetic algorithm for expression - Invariant face recognition

An experimental investigation on PCA based on cosine similarity and correlation for text feature dimensionality reduction

Linear and nonlinear dimensionality reduction for face recognition

SubXPCA versus PCA: A Theoretical Investigation

An Investigation on Recent Advances in Feature Partitioning Based Principal Component Analysis Methods

Similar Documents

An Empirical Study of Dimensionality Reduction Methods for Biometric Recognition

IRIS recognition based on PCA based Dimensionality reduction and SVM

An improved feature extraction method based on DWT and 2DSubXPCA methods

Dimensionality reduction based on Lorentzian Manifold for face recognition

A theoretical investigation of feature partitioning principal component analysis methods

Optimizing modular image PCA using Genetic algorithm for expression - Invariant face recognition

An experimental investigation on PCA based on cosine similarity and correlation for text feature dimensionality reduction

Linear and nonlinear dimensionality reduction for face recognition

SubXPCA versus PCA: A Theoretical Investigation

An Investigation on Recent Advances in Feature Partitioning Based Principal Component Analysis Methods