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Visualization and interpretation of models obtained with non-negative matrix factorization

Rok Gomišček (2015) Visualization and interpretation of models obtained with non-negative matrix factorization. MSc thesis.

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    Attributes that describe data in the databases present themselves in large numbers. For this reason defining truly important attributes for classification and establishing their mutual dependence poses a significant challenge. One way of reducing the dimensionality of the space and defining important attributes and examples is by using non-negative matrix factorization. In this master thesis we first examined the basics of non-negative matrix factorization and a few ways of visualizing the data and factor models in matrices. We propose a few ways of presenting and understanding the models acquired with factorization. We evaluated the effectiveness of the methods on several databases and learnt that each method reveals useful information about a model. Clustering of the factorized matrices can produce purer clusters than clustering of the source data. By projecting examples to the factor space we can see which factors affect certain classes. Adding attributes to this projection makes it possible to deduce the link between the examples and the attributes of the source space.

    Item Type: Thesis (MSc thesis)
    Keywords: non-negative matrix factorization, factor model, data visualization
    Number of Pages: 68
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    doc. dr. Tomaž Curk299Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1536577987)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3135
    Date Deposited: 16 Sep 2015 17:23
    Last Modified: 20 Oct 2015 11:00
    URI: http://eprints.fri.uni-lj.si/id/eprint/3135

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