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Statements

Subject Item
n2:RIV%2F61989592%3A15310%2F13%3A33147956%21RIV14-GA0-15310___
rdf:type
skos:Concept n17:Vysledek
dcterms:description
Boolean matrix factorization (BMF), or decomposition, received a considerable attention in data mining research, both for its direct usefulness in data analysis and its fundamental role in understanding Boolean data. In this paper, we argue that research should extend beyond the Boolean case toward more general type of data such as ordinal data. Technically, such extension amounts to replacement of the two-element Boolean algebra utilized in BMF by more general structures, which brings non-trivial challenges. We first present the problem formulation, survey the existing literature, and provide an illustrative example. Second, we present new theorems regarding decompositions of matrices with ordinal data. The theorems helps understand the geometry of decompositions and identify parts of input matrices which are good to focus on when computing factors. Third, we propose two algorithms based on these results along with an experimental evaluation. We conclude the paper with a discussion regarding future research issues. Boolean matrix factorization (BMF), or decomposition, received a considerable attention in data mining research, both for its direct usefulness in data analysis and its fundamental role in understanding Boolean data. In this paper, we argue that research should extend beyond the Boolean case toward more general type of data such as ordinal data. Technically, such extension amounts to replacement of the two-element Boolean algebra utilized in BMF by more general structures, which brings non-trivial challenges. We first present the problem formulation, survey the existing literature, and provide an illustrative example. Second, we present new theorems regarding decompositions of matrices with ordinal data. The theorems helps understand the geometry of decompositions and identify parts of input matrices which are good to focus on when computing factors. Third, we propose two algorithms based on these results along with an experimental evaluation. We conclude the paper with a discussion regarding future research issues.
dcterms:title
Beyond Boolean Matrix Decompositions: Toward Factor Analysis and Dimensionality Reduction of Ordinal Data Beyond Boolean Matrix Decompositions: Toward Factor Analysis and Dimensionality Reduction of Ordinal Data
skos:prefLabel
Beyond Boolean Matrix Decompositions: Toward Factor Analysis and Dimensionality Reduction of Ordinal Data Beyond Boolean Matrix Decompositions: Toward Factor Analysis and Dimensionality Reduction of Ordinal Data
skos:notation
RIV/61989592:15310/13:33147956!RIV14-GA0-15310___
n17:predkladatel
n18:orjk%3A15310
n3:aktivita
n8:P
n3:aktivity
P(GAP103/11/1456)
n3:dodaniDat
n19:2014
n3:domaciTvurceVysledku
n15:3476030 n15:9623264
n3:druhVysledku
n22:D
n3:duvernostUdaju
n13:S
n3:entitaPredkladatele
n16:predkladatel
n3:idSjednocenehoVysledku
63157
n3:idVysledku
RIV/61989592:15310/13:33147956
n3:jazykVysledku
n5:eng
n3:klicovaSlova
Ordinal Data; Reduction; Dimensionality; Factor; Boolean Matrix Decomposition
n3:klicoveSlovo
n4:Ordinal%20Data n4:Reduction n4:Boolean%20Matrix%20Decomposition n4:Dimensionality n4:Factor
n3:kontrolniKodProRIV
[85CED6FAA5FE]
n3:mistoKonaniAkce
Dallas
n3:mistoVydani
Los Alamos
n3:nazevZdroje
Proceedings of 2013 IEEE 13th International Conference on Data Mining
n3:obor
n20:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n21:GAP103%2F11%2F1456
n3:rokUplatneniVysledku
n19:2013
n3:tvurceVysledku
Bělohlávek, Radim Krmelová, Markéta
n3:typAkce
n6:WRD
n3:zahajeniAkce
2013-12-07+01:00
s:issn
1550-4786
s:numberOfPages
6
n23:doi
10.1109/ICDM.2013.127
n14:hasPublisher
IEEE Computer Society Press
n9:isbn
978-0-7685-5108-2
n11:organizacniJednotka
15310