Redukce dimensionality v Booleovských datech: Porovnání čtyř metod

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F15%3A33156523" target="_blank" >RIV/61989592:15310/15:33156523 - isvavai.cz</a>

Result on the web

<a href="http://link.springer.com/chapter/10.1007%2F978-3-662-48577-4_8" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-662-48577-4_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-662-48577-4_8" target="_blank" >10.1007/978-3-662-48577-4_8</a>

Result language

čeština

Original language name

Dimensionality Reduction in Boolean Data: Comparison of Four BMF Methods

Original language description

We compare four methods for Boolean matrix factorization (BMF). The oldest of these methods is the 8M method implemented in the BMDP statistical software package developed in the 1960s. The three other methods were developed recently. All the methods compute from an input object-attribute matrix I two matrices, namely an object-factor matrix A and a factor-attribute matrix B in such a way that the Boolean matrix product of A and B is approximately equal to I. Such decompositions are utilized directly inBoolean factor analysis or indirectly as a dimensionality reduction method for Boolean data in machine learning. While some comparison of the BMF methods with matrix decomposition methods designed for real valued data exists in the literature, a mutualcomparison of the various BMF methods is a severely neglected topic. In this paper, we compare the four methods on real datasets. In particular, we observe the reconstruction ability of the first few computed factors as well as the number

Czech name

Dimensionality Reduction in Boolean Data: Comparison of Four BMF Methods

Czech description

We compare four methods for Boolean matrix factorization (BMF). The oldest of these methods is the 8M method implemented in the BMDP statistical software package developed in the 1960s. The three other methods were developed recently. All the methods compute from an input object-attribute matrix I two matrices, namely an object-factor matrix A and a factor-attribute matrix B in such a way that the Boolean matrix product of A and B is approximately equal to I. Such decompositions are utilized directly inBoolean factor analysis or indirectly as a dimensionality reduction method for Boolean data in machine learning. While some comparison of the BMF methods with matrix decomposition methods designed for real valued data exists in the literature, a mutualcomparison of the various BMF methods is a severely neglected topic. In this paper, we compare the four methods on real datasets. In particular, we observe the reconstruction ability of the first few computed factors as well as the number

Type

D - Article in proceedings

CEP classification

IN - Informatics

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)

Publication year

2015

Confidentiality

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

Article name in the collection

Clustering High--Dimensional Data, Lecture Notes in Computer Science, col. 7627

ISBN

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ISSN

0302-9743

e-ISSN

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Number of pages

15

Pages from-to

118-133

Publisher name

Springer - Verlag Italia

Place of publication

Milano

Event location

Naples; Italy

Event date

May 15, 2012

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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