TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Sparse nonparametric topic model for transfer learning

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Kustantajai6doc.com publication
Sivut269-274
Sivumäärä6
ISBN (painettu)9782874190490
TilaJulkaistu - 2012
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012 - Bruges, Belgia
Kesto: 25 huhtikuuta 201227 huhtikuuta 2012

Conference

Conference20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012
MaaBelgia
KaupunkiBruges
Ajanjakso25/04/1227/04/12

Tiivistelmä

Count data arises for example in bioinformatics or analysis of text documents represented as word count vectors. With several data sets available from related sources, exploiting their similarities by transfer learning can improve models compared to modeling sources independently. We introduce a Bayesian generative transfer learning model which represents similarity across document collections by sparse sharing of latent topics controlled by an Indian Buffet Process. Unlike Hierarchical Dirichlet Process based multi-task learning, our model decouples topic sharing probability from topic strength, making sharing of low-strength topics easier, and outperforms the HDP approach in experiments.