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  Capricorn: An Algorithm for Subtropical Matrix Factorization

Karaev, S., & Miettinen, P. (2016). Capricorn: An Algorithm for Subtropical Matrix Factorization. In S. Chawla Venkatasubramanian, & W. Meira (Eds.), Proceedings of the Sixteenth SIAM International Conference on Data Mining (pp. 702-710). Philadelphia, PA: SIAM. doi:10.1137/1.9781611974348.79.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0029-542F-3 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-002B-A932-E
Genre: Konferenzbeitrag
Latex : Capricorn: {An} Algorithm for Subtropical Matrix Factorization

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 Urheber:
Karaev, Sanjar1, Autor           
Miettinen, Pauli1, Autor           
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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Schlagwörter: -
 Zusammenfassung: 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.

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Sprache(n): eng - English
 Datum: 201620162016
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: karaev16capricorn
DOI: 10.1137/1.9781611974348.79
 Art des Abschluß: -

Veranstaltung

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Titel: 16th SIAM International Conference on Data Mining
Veranstaltungsort: Miama, FL, USA
Start-/Enddatum: 2016-05-05 - 2016-05-07

Entscheidung

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Titel: Proceedings of the Sixteenth SIAM International Conference on Data Mining
  Kurztitel : SDM 2016
  Untertitel : Miami, Florida, USA, May 5 - May 7, 2016
Genre der Quelle: Konferenzband
 Urheber:
Chawla Venkatasubramanian, Sanjay1, Herausgeber
Meira, Wagner1, Herausgeber
Affiliations:
1 External Organizations, ou_persistent22            
Ort, Verlag, Ausgabe: Philadelphia, PA : SIAM
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 702 - 710 Identifikator: ISBN: 978-1-61197-434-8