Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

  • Bioinformatics Center, Institute for Chemical Research, Kyoto University

Kuvaus

Multi-view multi-task learning refers to dealing with dual-heterogeneous data,where each sample has multi-view features,and multiple tasks are correlated via common views.Existing methods do not sufficiently address three key challenges:(a) saving task correlation efficiently, (b) building a sparse model and (c) learning view-wise weights.In this paper, we propose a new method to directly handle these challenges based on multiplicative sparse feature decomposition.For (a), the weight matrix is decomposed into two components via low-rank constraint matrix factorization, which saves task correlation by learning a reduced number of model parameters.For (b) and (c), the first component is further decomposed into two sub-components,to select topic-specific features and learn view-wise importance, respectively. Theoretical analysis reveals its equivalence with a general form of joint regularization,and motivates us to develop a fast optimization algorithm in a linear complexity w.r.t. the data size.Extensive experiments on both simulated and real-world datasets validate its efficiency.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Joint Conference on Artificial Intelligence - Macao, Kiina
Kesto: 10 elokuuta 201916 elokuuta 2019
Konferenssinumero: 28
https://ijcai19.org/

Conference

ConferenceInternational Joint Conference on Artificial Intelligence
LyhennettäIJCAI
MaaKiina
KaupunkiMacao
Ajanjakso10/08/201916/08/2019
www-osoite

ID: 36886720