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Statements

Subject Item
n2:RIV%2F61989592%3A15310%2F11%3A10225061%21RIV12-GA0-15310___
rdf:type
skos:Concept n5:Vysledek
dcterms:description
In the paper we deal with dimensionality reduction techniques for a dataset with discrete attributes. Dimensionality reduction is considered as one of the most important problems in data analysis. The main aim of our paper is to show advantages of a novel approach introduced and developed by Belohlavek and Vychodil in comparison of two classical dimensionality reduction methods which can be used for ordinal attributes (CATPCA and factor analysis). The novel technique is fundamentally different from existing ones since it is based on another kind of mathematical apparatus (namely, Galois connections, lattice theory, fuzzy logic). Therefore, this method is able to bring a new insight to examined data. The comparison is accompanied by analysis of two data sets which were obtained by questionnaire survey. In the paper we deal with dimensionality reduction techniques for a dataset with discrete attributes. Dimensionality reduction is considered as one of the most important problems in data analysis. The main aim of our paper is to show advantages of a novel approach introduced and developed by Belohlavek and Vychodil in comparison of two classical dimensionality reduction methods which can be used for ordinal attributes (CATPCA and factor analysis). The novel technique is fundamentally different from existing ones since it is based on another kind of mathematical apparatus (namely, Galois connections, lattice theory, fuzzy logic). Therefore, this method is able to bring a new insight to examined data. The comparison is accompanied by analysis of two data sets which were obtained by questionnaire survey.
dcterms:title
Comparison of classical dimensionality reduction methods with novel approach based on formal concept analysis Comparison of classical dimensionality reduction methods with novel approach based on formal concept analysis
skos:prefLabel
Comparison of classical dimensionality reduction methods with novel approach based on formal concept analysis Comparison of classical dimensionality reduction methods with novel approach based on formal concept analysis
skos:notation
RIV/61989592:15310/11:10225061!RIV12-GA0-15310___
n5:predkladatel
n6:orjk%3A15310
n3:aktivita
n16:P
n3:aktivity
P(GAP202/10/0262)
n3:cisloPeriodika
1
n3:dodaniDat
n8:2012
n3:domaciTvurceVysledku
n15:5733685
n3:druhVysledku
n17:J
n3:duvernostUdaju
n9:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
191082
n3:idVysledku
RIV/61989592:15310/11:10225061
n3:jazykVysledku
n18:eng
n3:klicovaSlova
dimensionality reduction, discrete data, factor analysis, formal concept analysis, fuzzy logic, matrix decomposition, principal component analysis
n3:klicoveSlovo
n10:factor%20analysis n10:principal%20component%20analysis n10:formal%20concept%20analysis n10:fuzzy%20logic n10:dimensionality%20reduction n10:discrete%20data n10:matrix%20decomposition
n3:kodStatuVydavatele
DE - Spolková republika Německo
n3:kontrolniKodProRIV
[3945B21579C7]
n3:nazevZdroje
Lecture Notes in Computer Science
n3:obor
n4:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:projekt
n12:GAP202%2F10%2F0262
n3:rokUplatneniVysledku
n8:2011
n3:svazekPeriodika
6954
n3:tvurceVysledku
Bartl, Eduard Sobíšek, Lukáš Řezánková, Hana
s:issn
0302-9743
s:numberOfPages
10
n19:organizacniJednotka
15310