Proceedings Article, Paper
@InProceedings
Beitrag in Tagungsband, Workshop


Show entries of:

this year (2014) | last year (2013) | two years ago (2012) | Notes URL

Action:

login to update

Options:




Library Locked Library locked




Author, Editor

Author(s):

Miettinen, Pauli

dblp



Editor(s):

Cook, Diane
Pei, Jian
Wang, Wei
Za\"{i}ane, Osmar
Wu, Xindong

dblp
dblp
dblp
dblp
dblp

Not MPII Editor(s):

Cook, Diane
Pei, Jian
Wang, Wei
Za\"{i}ane, Osmar
Wu, Xindong

BibTeX cite key*:

miettinen11boolean

Title, Booktitle

Title*:

Boolean Tensor Factorization


ICDM '11 2011 Miettinen.pdf (276.84 KB)

Booktitle*:

11th IEEE International Conference on Data Mining : ICDM 2011

Event, URLs

URL of the conference:

http://icdm2011.cs.ualberta.ca/

URL for downloading the paper:

http://dx.doi.org/ 10.1109/ICDM.2011.28

Event Address*:

Vancouver, Canada

Language:

English

Event Date*
(no longer used):


Organization:

IEEE Computer Society

Event Start Date:

20 December 2011

Event End Date:

20 December 2011

Publisher

Name*:

IEEE

URL:


Address*:

Los Alamitos, CA

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:


Pages:

447-456

Year*:

2011

VG Wort Pages:


ISBN/ISSN:

978-0-7695-4408-3

Sequence Number:


DOI:

10.1109/ICDM.2011.28



Note, Abstract, ©


(LaTeX) Abstract:

Tensors are multi-way generalizations of matrices, and similarly to matrices, they can also be factorized, that is, represented (approximately) as a product of factors. These factors are typically either all matrices or a mixture of matrices and tensors. With the widespread adoption of matrix factorization techniques in data mining, also tensor factorizations have started to gain attention.

In this paper we study the Boolean tensor factorizations. We assume that the data is binary multi-way data, and we want to factorize it to binary factors using Boolean arithmetic (i.e.\ defining that $1+1=1$). Boolean tensor factorizations are, therefore, natural generalization of the Boolean matrix factorizations. We will study the theory of Boolean tensor factorizations and show that at least some of the benefits Boolean matrix factorizations have over normal matrix factorizations carry over to the tensor data. We will also present algorithms for Boolean variations of CP and Tucker decompositions, the two most-common types of tensor factorizations. With experimentation done with synthetic and real-world data, we show that Boolean tensor factorizations are a viable alternative when the data is naturally binary.

Copyright Message:

Copyright 2011 IEEE.


Download
Access Level:

Public

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Databases and Information Systems Group

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{miettinen11boolean,
AUTHOR = {Miettinen, Pauli},
EDITOR = {Cook, Diane and Pei, Jian and Wang, Wei and Za\"{i}ane, Osmar and Wu, Xindong},
TITLE = {Boolean Tensor Factorization},
BOOKTITLE = {11th IEEE International Conference on Data Mining : ICDM 2011},
PUBLISHER = {IEEE},
YEAR = {2011},
ORGANIZATION = {IEEE Computer Society},
PAGES = {447--456},
ADDRESS = {Vancouver, Canada},
ISBN = {978-0-7695-4408-3},
DOI = {10.1109/ICDM.2011.28},
}


Entry last modified by Klaus Berberich, 02/21/2013
Show details for Edit History (please click the blue arrow to see the details)Edit History (please click the blue arrow to see the details)
Hide details for Attachment SectionAttachment Section

View attachments here:


File Attachment Icon
ICDM '11 2011 Miettinen.pdf