de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
 
 
DownloadE-Mail
  Getting to Know the Unknown Unknowns: Destructive-noise Resistant Boolean Matrix Factorization

Karaev, S., Miettinen, P., & Vreeken, J. (2015). Getting to Know the Unknown Unknowns: Destructive-noise Resistant Boolean Matrix Factorization. In S. Venkatasubramanian, & J. Ye (Eds.), Proceedings of the 2015 SIAM International Conference on Data Mining (pp. 325-333). Philadelphia, PA: SIAM. doi:10.1137/1.9781611974010.37.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0024-6C59-C Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0028-8542-0
Genre: Conference Paper
Latex : Getting to Know the Unknown Unknowns: {D}estructive-noise Resistant {Boolean} Matrix Factorization

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Karaev, Sanjar1, Author              
Miettinen, Pauli1, Author              
Vreeken, Jilles1, Author              
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, escidoc:24018              

Content

show
hide
Free keywords: -
 Abstract: Finding patterns from binary data is a classical problem in data mining, dating back to at least frequent itemset mining. More recently, approaches such as tiling and Boolean matrix factorization (BMF), have been proposed to find sets of patterns that aim to explain the full data well. These methods, however, are not robust against non-trivial destructive noise, i.e. when relatively many 1s are removed from the data: tiling can only model additive noise while BMF assumes approximately equal amounts of additive and destructive noise. Most real-world binary datasets, however, exhibit mostly destructive noise. In presence/absence data, for instance, it is much more common to fail to observe something than it is to observe a spurious presence. To address this problem, we take the recent approach of employing the Minimum Description Length (MDL) principle for BMF and introduce a new algorithm, Nassau, that directly optimizes the description length of the factorization instead of the reconstruction error. In addition, unlike the previous algorithms, it can adjust the factors it has discovered during its search. Empirical evaluation on synthetic data shows that Nassau excels at datasets with high destructive noise levels and its performance on real-world datasets confirms our hypothesis of the high numbers of missing observations in the real-world data.

Details

show
hide
Language(s): eng - English
 Dates: 2014-12-1620152015
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: karaev15getting
DOI: 10.1137/1.9781611974010.37
 Degree: -

Event

show
hide
Title: 15th SIAM International Conference on Data Mining
Place of Event: Vancouver, Canada
Start-/End Date: 2015-04-30 - 2015-05-02

Legal Case

show

Project information

show

Source 1

show
hide
Title: Proceedings of the 2015 SIAM International Conference on Data Mining
  Abbreviation : SDM 2015
Source Genre: Proceedings
 Creator(s):
Venkatasubramanian, Suresh1, Editor
Ye, Jieping1, Editor
Affiliations:
1 External Organizations, escidoc:persistent22            
Publ. Info: Philadelphia, PA : SIAM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 325 - 333 Identifier: ISBN: 978-1-61197-401-0