Factor analysis of incidence data via novel decomposition of matrices

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F09%3A00010281" target="_blank" >RIV/61989592:15310/09:00010281 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Factor analysis of incidence data via novel decomposition of matrices

Popis výsledku v původním jazyce

Matrix decomposition methods provide representations of anobject-variable data matrix by a product of two different matrices, onedescribing relationship between objects and hidden variables or factors,and the other describing relationship between the factors and the originalvariables. We present a novel approach to decomposition and factoranalysis of matrices with incidence data. The matrix entries are gradesto which objects represented by rows satisfy attributes represented bycolumns, e.g. grades to which an image is red or a person performs wellin a test. We assume that the grades belong to a scale bounded by 0and 1 which is equipped with certain aggregation operators and forms acomplete residuated lattice. We present an approximation algorithm forthe problem of decomposition of such matrices with grades into productsof two matrices with grades with the number of factors as smallas possible. Decomposition of binary matrices into Boolean products ofbinary matrices is a special case of

Název v anglickém jazyce

Factor analysis of incidence data via novel decomposition of matrices

Popis výsledku anglicky

Matrix decomposition methods provide representations of anobject-variable data matrix by a product of two different matrices, onedescribing relationship between objects and hidden variables or factors,and the other describing relationship between the factors and the originalvariables. We present a novel approach to decomposition and factoranalysis of matrices with incidence data. The matrix entries are gradesto which objects represented by rows satisfy attributes represented bycolumns, e.g. grades to which an image is red or a person performs wellin a test. We assume that the grades belong to a scale bounded by 0and 1 which is equipped with certain aggregation operators and forms acomplete residuated lattice. We present an approximation algorithm forthe problem of decomposition of such matrices with grades into productsof two matrices with grades with the number of factors as smallas possible. Decomposition of binary matrices into Boolean products ofbinary matrices is a special case of

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BD - Teorie informace

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název periodika

Lecture Notes in Artificial Intelligence

ISSN

0302-9743

e-ISSN

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Svazek periodika

5548

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

15

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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