Identification Information Analysis of Sample Train Set Subspace

Principal component analysis (PCA) which is widely used in pattern recognition field aims at reducing the dimension of sample. PCA replaces variables in the original sample vectors that have redundant information with fewer integrative variables. The recognition ability used author's algorithm...

Full description

Saved in:
Bibliographic Details
Main Authors: XiangFei Fu, Jiliu Zhou, FangNian Lang
Format: Conference Proceeding
Language:English
Subjects:
Eigenvalues and eigenfunctions
Face recognition
Feature extraction
Frequency
Image coding
Information analysis
Partitioning algorithms
Pattern recognition
Principal component analysis
Testing
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 638
container_issue
container_start_page 633
container_title
container_volume
creator XiangFei Fu
Jiliu Zhou
FangNian Lang
description Principal component analysis (PCA) which is widely used in pattern recognition field aims at reducing the dimension of sample. PCA replaces variables in the original sample vectors that have redundant information with fewer integrative variables. The recognition ability used author's algorithm is tested in the paper. It is proved that zerospace do not include any identification information which would be useful for distinguishing different samples. Experiment results based of our lab's facebase and ORL face base shows the theory is right.
doi_str_mv 10.1109/ICIG.2007.167
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4297160</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4297160</ieee_id><sourcerecordid>4297160</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-81a92ddaae89e35bf0f673a6dad6bedc3963836a688e21f4d9d2bff5c3cde2b93</originalsourceid><addsrcrecordid>eNotjLtqwzAUQAWl0JJ67NRFP2BXD1vS3RpMmxoCGZzO4dq6AhW_sNwhf99CepZzpsPYsxSFlAJem7o5FEoIW0hj71gG1glroFKgoHpgWUrf4g8NpVXukb01nqYthtjjFueJN1OY1_HW-wmHa4qJz4G3OC4D8fOKceItbbz96dKCPT2x-4BDouzfO_b18X6uP_Pj6dDU-2Mepa223EkE5T0iOSBddUEEYzUaj9505HsNRjtt0DhHSobSg1ddCFWve0-qA71jL7dvJKLLssYR1-ulVGClEfoXBdBItw</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Identification Information Analysis of Sample Train Set Subspace</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>XiangFei Fu ; Jiliu Zhou ; FangNian Lang</creator><creatorcontrib>XiangFei Fu ; Jiliu Zhou ; FangNian Lang</creatorcontrib><description>Principal component analysis (PCA) which is widely used in pattern recognition field aims at reducing the dimension of sample. PCA replaces variables in the original sample vectors that have redundant information with fewer integrative variables. The recognition ability used author's algorithm is tested in the paper. It is proved that zerospace do not include any identification information which would be useful for distinguishing different samples. Experiment results based of our lab's facebase and ORL face base shows the theory is right.</description><identifier>ISBN: 9780769529295</identifier><identifier>ISBN: 0769529291</identifier><identifier>DOI: 10.1109/ICIG.2007.167</identifier><language>eng</language><publisher>IEEE</publisher><subject>Eigenvalues and eigenfunctions ; Face recognition ; Feature extraction ; Frequency ; Image coding ; Information analysis ; Partitioning algorithms ; Pattern recognition ; Principal component analysis ; Testing</subject><ispartof>Fourth International Conference on Image and Graphics (ICIG 2007), 2007, p.633-638</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4297160$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27923,54918</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4297160$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>XiangFei Fu</creatorcontrib><creatorcontrib>Jiliu Zhou</creatorcontrib><creatorcontrib>FangNian Lang</creatorcontrib><title>Identification Information Analysis of Sample Train Set Subspace</title><title>Fourth International Conference on Image and Graphics (ICIG 2007)</title><addtitle>ICIG</addtitle><description>Principal component analysis (PCA) which is widely used in pattern recognition field aims at reducing the dimension of sample. PCA replaces variables in the original sample vectors that have redundant information with fewer integrative variables. The recognition ability used author's algorithm is tested in the paper. It is proved that zerospace do not include any identification information which would be useful for distinguishing different samples. Experiment results based of our lab's facebase and ORL face base shows the theory is right.</description><subject>Eigenvalues and eigenfunctions</subject><subject>Face recognition</subject><subject>Feature extraction</subject><subject>Frequency</subject><subject>Image coding</subject><subject>Information analysis</subject><subject>Partitioning algorithms</subject><subject>Pattern recognition</subject><subject>Principal component analysis</subject><subject>Testing</subject><isbn>9780769529295</isbn><isbn>0769529291</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjLtqwzAUQAWl0JJ67NRFP2BXD1vS3RpMmxoCGZzO4dq6AhW_sNwhf99CepZzpsPYsxSFlAJem7o5FEoIW0hj71gG1glroFKgoHpgWUrf4g8NpVXukb01nqYthtjjFueJN1OY1_HW-wmHa4qJz4G3OC4D8fOKceItbbz96dKCPT2x-4BDouzfO_b18X6uP_Pj6dDU-2Mepa223EkE5T0iOSBddUEEYzUaj9505HsNRjtt0DhHSobSg1ddCFWve0-qA71jL7dvJKLLssYR1-ulVGClEfoXBdBItw</recordid><startdate>200708</startdate><enddate>200708</enddate><creator>XiangFei Fu</creator><creator>Jiliu Zhou</creator><creator>FangNian Lang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200708</creationdate><title>Identification Information Analysis of Sample Train Set Subspace</title><author>XiangFei Fu ; Jiliu Zhou ; FangNian Lang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-81a92ddaae89e35bf0f673a6dad6bedc3963836a688e21f4d9d2bff5c3cde2b93</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Eigenvalues and eigenfunctions</topic><topic>Face recognition</topic><topic>Feature extraction</topic><topic>Frequency</topic><topic>Image coding</topic><topic>Information analysis</topic><topic>Partitioning algorithms</topic><topic>Pattern recognition</topic><topic>Principal component analysis</topic><topic>Testing</topic><toplevel>online_resources</toplevel><creatorcontrib>XiangFei Fu</creatorcontrib><creatorcontrib>Jiliu Zhou</creatorcontrib><creatorcontrib>FangNian Lang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>XiangFei Fu</au><au>Jiliu Zhou</au><au>FangNian Lang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Identification Information Analysis of Sample Train Set Subspace</atitle><btitle>Fourth International Conference on Image and Graphics (ICIG 2007)</btitle><stitle>ICIG</stitle><date>2007-08</date><risdate>2007</risdate><spage>633</spage><epage>638</epage><pages>633-638</pages><isbn>9780769529295</isbn><isbn>0769529291</isbn><abstract>Principal component analysis (PCA) which is widely used in pattern recognition field aims at reducing the dimension of sample. PCA replaces variables in the original sample vectors that have redundant information with fewer integrative variables. The recognition ability used author's algorithm is tested in the paper. It is proved that zerospace do not include any identification information which would be useful for distinguishing different samples. Experiment results based of our lab's facebase and ORL face base shows the theory is right.</abstract><pub>IEEE</pub><doi>10.1109/ICIG.2007.167</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISBN: 9780769529295
ispartof Fourth International Conference on Image and Graphics (ICIG 2007), 2007, p.633-638
issn
language eng
recordid cdi_ieee_primary_4297160
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Eigenvalues and eigenfunctions
Face recognition
Feature extraction
Frequency
Image coding
Information analysis
Partitioning algorithms
Pattern recognition
Principal component analysis
Testing
title Identification Information Analysis of Sample Train Set Subspace
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T01%3A06%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Identification%20Information%20Analysis%20of%20Sample%20Train%20Set%20Subspace&rft.btitle=Fourth%20International%20Conference%20on%20Image%20and%20Graphics%20(ICIG%202007)&rft.au=XiangFei%20Fu&rft.date=2007-08&rft.spage=633&rft.epage=638&rft.pages=633-638&rft.isbn=9780769529295&rft.isbn_list=0769529291&rft_id=info:doi/10.1109/ICIG.2007.167&rft_dat=%3Cieee_6IE%3E4297160%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-81a92ddaae89e35bf0f673a6dad6bedc3963836a688e21f4d9d2bff5c3cde2b93%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4297160&rfr_iscdi=true