Comparison of Neural Network Boolean Factor Analysis Method with Some Other Dimension Reduction Methods on Bars Problem

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F07%3A00091120" target="_blank" >RIV/67985807:_____/07:00091120 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27240/07:00017860

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Comparison of Neural Network Boolean Factor Analysis Method with Some Other Dimension Reduction Methods on Bars Problem

Original language description

In this paper, we compare performance of novel neural network based algorithm for Boolean factor analysis with several dimension reduction techniques as a tool for feature extraction. Compared are namely singular value decomposition, semi-discrete decomposition and non-negative matrix factorization algorithms, including some cluster analysis methods as well. Even if the mainly mentioned methods are linear, it is interesting to compare them with neural network based Boolean factor analysis, because theyare well elaborated. Second reason for this is to show basic differences between Boolean and linear case. So called bars problem is used as the benchmark. Set of artificial signals generated as a Boolean sum of given number of bars is analyzed by these methods. Resulting images show that Boolean factor analysis is upmost suitable method for this kind of data.

Czech name

Srovnání na neuronovém přístupu založené Booleavské faktorové analýzy a dalších metod pro redukci dimenze na problému kolmých protínajících se linií

Czech description

Porovnán je nový algoritmus pro Booleovskou faktorovou analýzu s několika dalšími metodami pro redukci dimenze jako možného nástroje pro extrakci příznaků. Porovnávány jsou zejména metody SVD, FastMap, SDD, NMF včetně některých metod shlukové analýzy. Pro hodnocení je použita referenční úloha separace kolmých linií (tzv. Bar Problem). Je ukázáno, že Booleovská faktorová analýza je ze své podstaty nejvhodnější nástroj tento typ dat.

Type

D - Article in proceedings

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2007

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Pattern Recognition and Machine Intelligence

ISBN

978-3-540-77045-9

ISSN

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e-ISSN

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Number of pages

9

Pages from-to

235-243

Publisher name

Springer

Place of publication

Berlin

Event location

Kolkata

Event date

Dec 18, 2007

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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