TY - GEN
T1 - Bayesian Independent Component Analysis under Hierarchical Model on Independent Components
AU - Asaba, Kai
AU - Saito, Shota
AU - Horii, Shunsuke
AU - Matsushima, Toshiyasu
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported in part by JSPS KAKENHI Grant Numbers JP16K00195, JP16K00417, JP17K00316, and JP17K06446.
PY - 2019/3/4
Y1 - 2019/3/4
N2 - Independent component analysis (ICA) deals with the problem of estimating unknown latent variables (independent components) from observed data. One of the previous studies of ICA assumes a Laplace distribution on independent components. However, this assumption makes it difficult to calculate the posterior distribution of independent components. On the other hand, in the problem of sparse linear regression, several studies have approximately calculated the posterior distribution of parameters by assuming a hierarchical model expressing a Laplace distribution. This paper considers ICA in which a hierarchical model expressing a Laplace distribution is assumed on independent components. For this hierarchical model, we propose a method of calculating the approximate posterior distribution of independent components by using a variational Bayes method. Through some experiments, we show the effectiveness of our proposed method.
AB - Independent component analysis (ICA) deals with the problem of estimating unknown latent variables (independent components) from observed data. One of the previous studies of ICA assumes a Laplace distribution on independent components. However, this assumption makes it difficult to calculate the posterior distribution of independent components. On the other hand, in the problem of sparse linear regression, several studies have approximately calculated the posterior distribution of parameters by assuming a hierarchical model expressing a Laplace distribution. This paper considers ICA in which a hierarchical model expressing a Laplace distribution is assumed on independent components. For this hierarchical model, we propose a method of calculating the approximate posterior distribution of independent components by using a variational Bayes method. Through some experiments, we show the effectiveness of our proposed method.
UR - http://www.scopus.com/inward/record.url?scp=85063425549&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063425549&partnerID=8YFLogxK
U2 - 10.23919/APSIPA.2018.8659578
DO - 10.23919/APSIPA.2018.8659578
M3 - Conference contribution
AN - SCOPUS:85063425549
T3 - 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
SP - 959
EP - 962
BT - 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
Y2 - 12 November 2018 through 15 November 2018
ER -