Prikaz in tolmačenje modelov nenegativne matrične faktorizacije
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.
2015
2015-09-29 05:11:34
9999
nenegativna matrična faktorizacija, faktorski model, vizualizacija podatkov
non-negative matrix factorization, factor model, data visualization
r6
Rok
Gomišček
70
Tomaž
Curk
991
COBISS_ID
3
1536577987
0
Predstavitvena datoteka
2015-09-29 05:11:34