TY - GEN
T1 - Non-parametric e-mixture of density functions
AU - Hino, Hideitsu
AU - Takano, Ken
AU - Akaho, Shotaro
AU - Murata, Noboru
PY - 2016
Y1 - 2016
N2 - Mixture modeling is one of the simplest ways to represent complicated probability density functions, and to integrate information from different sources. There are two typical mixtures in the context of information geometry, the m-and e-mixtures. This paper proposes a novel framework of non-parametric e-mixture modeling by using a simple estimation algorithm based on geometrical insights into the characteristics of the e-mixture. An experimental result supports the proposed framework.
AB - Mixture modeling is one of the simplest ways to represent complicated probability density functions, and to integrate information from different sources. There are two typical mixtures in the context of information geometry, the m-and e-mixtures. This paper proposes a novel framework of non-parametric e-mixture modeling by using a simple estimation algorithm based on geometrical insights into the characteristics of the e-mixture. An experimental result supports the proposed framework.
KW - Information geometry
KW - Mixture model
KW - Non-parametric method
UR - http://www.scopus.com/inward/record.url?scp=84992666022&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992666022&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46672-9_1
DO - 10.1007/978-3-319-46672-9_1
M3 - Conference contribution
AN - SCOPUS:84992666022
SN - 9783319466712
VL - 9948 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 10
BT - Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
PB - Springer Verlag
T2 - 23rd International Conference on Neural Information Processing, ICONIP 2016
Y2 - 16 October 2016 through 21 October 2016
ER -