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ICDM
2010
IEEE
160views Data Mining» more  ICDM 2010»
13 years 9 months ago
A Privacy Preserving Framework for Gaussian Mixture Models
Abstract--This paper presents a framework for privacypreserving Gaussian Mixture Model computations. Specifically, we consider a scenario where a central service wants to learn the...
Madhusudana Shashanka
SSPR
2010
Springer
13 years 10 months ago
Non-parametric Mixture Models for Clustering
Mixture models have been widely used for data clustering. However, commonly used mixture models are generally of a parametric form (e.g., mixture of Gaussian distributions or GMM),...
Pavan Kumar Mallapragada, Rong Jin, Anil K. Jain
IVC
2010
128views more  IVC 2010»
13 years 10 months ago
Online kernel density estimation for interactive learning
In this paper we propose a Gaussian-kernel-based online kernel density estimation which can be used for applications of online probability density estimation and online learning. ...
Matej Kristan, Danijel Skocaj, Ales Leonardis
CSDA
2007
84views more  CSDA 2007»
13 years 11 months ago
Mixtures of spatial and unstructured effects for spatially discontinuous health outcomes
This paper proposes mixture models for spatially adaptive smoothing of health event data (e.g. mortality or illness totals). Such models allow for spatial pooling of strength but a...
Peter Congdon
NN
2002
Springer
136views Neural Networks» more  NN 2002»
13 years 11 months ago
Bayesian model search for mixture models based on optimizing variational bounds
When learning a mixture model, we suffer from the local optima and model structure determination problems. In this paper, we present a method for simultaneously solving these prob...
Naonori Ueda, Zoubin Ghahramani
JAIR
1998
198views more  JAIR 1998»
13 years 11 months ago
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Alberto Ruiz, Pedro E. López-de-Teruel, M. ...
PRL
2000
58views more  PRL 2000»
13 years 11 months ago
Learning mixture models using a genetic version of the EM algorithm
The need to
Aleix M. Martínez, Jordi Vitrià
PAMI
2008
161views more  PAMI 2008»
13 years 11 months ago
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models
The Expectation Maximization (EM) algorithm is widely used for learning finite mixture models despite its greedy nature. Most popular model-based clustering techniques might yield...
Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratn...
CSDA
2007
81views more  CSDA 2007»
13 years 11 months ago
A stochastic EM algorithm for a semiparametric mixture model
Recently, there has been a considerable interest in finite mixture models with semi-/non-parametric component distributions. Identifiability of such model parameters is generall...
Laurent Bordes, Didier Chauveau, Pierre Vandekerkh...
IR
2006
13 years 11 months ago
A study of mixture models for collaborative filtering
Collaborative filtering is a general technique for exploiting the preference patterns of a group of users to predict the utility of items for a particular user. Three different co...
Rong Jin, Luo Si, Chengxiang Zhai