Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46672-9_1 | ICONIP |
Keywords | Field | DocType |
Mixture model,Information geometry,Non-parametric method | Information geometry,Mixture modeling,Computer science,Nonparametric statistics,Artificial intelligence,Probability density function,Mixture model,Machine learning | Conference |
Volume | ISSN | Citations |
9948 | 0302-9743 | 1 |
PageRank | References | Authors |
0.38 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hideitsu Hino | 1 | 99 | 25.73 |
| Ken Takano | 2 | 1 | 0.38 |
| Shotaro Akaho | 3 | 650 | 79.46 |
| Noboru Murata | 4 | 855 | 170.36 |