Non-parametric e-mixture of Density Functions

被引：0

作者：

Hino, Hideitsu ^{[1
]}

Takano, Ken ^{[2
]}

Akaho, Shotaro ^{[3
]}

Murata, Noboru ^{[2
]}

机构：

[1] Univ Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058573, Japan

[2] Waseda Univ, Shinjuku Ku, Okubo 3-4-1, Tokyo 1698555, Japan

[3] AIST, 1-1-1 Umezono, Tsukuba, Ibaraki 3058568, Japan

来源：

NEURAL INFORMATION PROCESSING, ICONIP 2016, PT II | 2016年 / 9948卷

关键词：

Mixture model; Information geometry; Non-parametric method; DIVERGENCE ESTIMATION;

D O I：

10.1007/978-3-319-46672-9_1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：3 / 10

页数：8

共 50 条

[1] Bayesian prediction of a density function in terms of e-mixture
Yanagimoto, Takemi
Ohnishi, Toshio
[J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2009, 139 (09) : 3064 - 3075
[2] ON NON-PARAMETRIC ESTIMATES OF DENSITY FUNCTIONS AND REGRESSION CURVES
NADARAYA, EA
[J]. THEORY OF PROBILITY AND ITS APPLICATIONS,USSR, 1965, 10 (01): : 186 - &
[3] The Use of Non-Parametric Methods to Estimate Density Functions of Copulas
Hmood, Munaf Yousif
Hamza, Zainab Falih
[J]. JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, 2019, 14 (04): : 492 - 507
[4] REMARKS ON NON-PARAMETRIC ESTIMATES FOR DENSITY FUNCTIONS AND REGRESSION CURVES
NADARAYA, EA
[J]. THEORY OF PROBILITY AND ITS APPLICATIONS,USSR, 1970, 15 (01): : 134 - &
[5] Nonparametric e-Mixture Estimation
Takano, Ken
Hino, Hideitsu
Akaho, Shotaro
Murata, Noboru
[J]. NEURAL COMPUTATION, 2016, 28 (12) : 2687 - 2725
[6] Non-parametric Mixture Models for Clustering
Mallapragada, Pavan Kumar
Jin, Rong
Jain, Anil
[J]. STRUCTURAL, SYNTACTIC, AND STATISTICAL PATTERN RECOGNITION, 2010, 6218 : 334 - 343
[7] An adaptive approach to non-parametric estimation of dynamic probability density functions
Pana, Cristian
Severi, Stefano
de Abreu, Giuseppe Thadeu Freitas
[J]. PROCEEDINGS OF THE 2016 13TH WORKSHOP ON POSITIONING, NAVIGATION AND COMMUNICATIONS (WPNC), 2016,
[8] Non-parametric Bayesian inference for continuous density hidden Markov mixture model
Bathaee, Najmeh
Sheikhzadeh, Hamid
[J]. STATISTICAL METHODOLOGY, 2016, 33 : 256 - 275
[9] Non-parametric k-sample tests: Density functions vs distribution functions
Martinez-Camblor, Pablo
de Una-Alvarez, Jacobo
[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2009, 53 (09) : 3344 - 3357
[10] Non-parametric estimation of mixture model order
Corona, Enrique
Nutter, Brian
Mitra, Sunanda
[J]. 2008 IEEE SOUTHWEST SYMPOSIUM ON IMAGE ANALYSIS & INTERPRETATION, 2008, : 145 - 148

← 1 2 3 4 5 →