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Author, Editor

Author(s):

Miettinen, Pauli
Vreeken, Jilles

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Not MPG Author(s):

Vreeken, Jilles

Editor(s):





BibTeX cite key*:

miettinen11model

Title, Booktitle

Title*:

Model Order Selection for Boolean Matrix Factorization


op0629-miettinen.pdf (1568.15 KB)

Booktitle*:

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'11)

Event, URLs

URL of the conference:

http://www.kdd.org/kdd2011/

URL for downloading the paper:

http://doi.acm.org/10.1145/2020408.2020424

Event Address*:

San Diego, CA, USA

Language:

English

Event Date*
(no longer used):


Organization:

Association of Computing Machinery (ACM)

Event Start Date:

20 December 2011

Event End Date:

20 December 2011

Publisher

Name*:

ACM

URL:


Address*:

New York, NY

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:


Pages:

51-59

Year*:

2011

VG Wort Pages:


ISBN/ISSN:

978-1-4503-0813-7

Sequence Number:


DOI:

10.1145/2020408.2020424



Note, Abstract, ©


(LaTeX) Abstract:

Matrix factorizations---where a given data matrix is approximated by a product of two or more factor matrices---are powerful data mining tools. Among other tasks, matrix factorizations are often used to separate global structure from noise. This, however, requires solving the `model order selection problem' of determining where fine-grained structure stops, and noise starts, i.e., what is the proper size of the factor matrices.

Boolean matrix factorization (BMF)---where data, factors, and matrix product are Boolean---has received increased attention from the data mining community in recent years. The technique has
desirable properties, such as high interpretability and natural sparsity. But so far no method for selecting the correct model order for BMF has been available.
In this paper we propose to use the Minimum Description Length (MDL) principle for this task. Besides solving the problem, this well-founded approach has numerous benefits, e.g., it is automatic, does not require a likelihood function, is fast, and, as experiments show, is highly accurate.

We formulate the description length function for BMF in general---making it applicable for any BMF algorithm. We extend an existing algorithm for BMF to use MDL to identify the best Boolean matrix factorization, analyze the complexity of the problem, and perform an extensive experimental evaluation to study its behavior.

Copyright Message:

(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2011), http://doi.acm.org/10.1145/2020408.2020424.


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Access Level:

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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{miettinen11model,
AUTHOR = {Miettinen, Pauli and Vreeken, Jilles},
TITLE = {Model Order Selection for {Boolean} Matrix Factorization},
BOOKTITLE = {Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'11)},
PUBLISHER = {ACM},
YEAR = {2011},
ORGANIZATION = {Association of Computing Machinery (ACM)},
PAGES = {51--59},
ADDRESS = {San Diego, CA, USA},
ISBN = {978-1-4503-0813-7},
DOI = {10.1145/2020408.2020424},
}


Entry last modified by Anja Becker, 03/22/2012
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Editor(s)
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Created
12/20/2011 01:54:48 PM
Revisions
2.
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Editor(s)
Anja Becker
Anja Becker
Pauli Miettinen

Edit Dates
22.03.2012 14:58:06
13.03.2012 14:04:43
20.12.2011 13.54.48

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