Sorry, you need to enable JavaScript to visit this website.

Unsupervised Learning

Self-paced mixture of t distribution model


Gaussian mixture model (GMM) is a powerful probabilistic model for representing the probability distribution of observations in the population. However, the fitness of Gaussian mixture model can be significantly degraded when the data contain a certain amount of outliers. Although there are certain variants of GMM (e.g., mixture of Laplace, mixture of t distribution) attempting to handle outliers, none of them can sufficiently mitigate the effect of outliers if the outliers are far from the centroids.

Paper Details

Authors:
Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia
Submitted On:
27 May 2018 - 10:23pm
Short Link:
Type:
Event:
Paper Code:
Document Year:
Cite

Document Files

icassp-landscape.pdf

(161)

Keywords

Additional Categories

Subscribe

[1] Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia, "Self-paced mixture of t distribution model", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3210. Accessed: Oct. 18, 2019.
@article{3210-18,
url = {http://sigport.org/3210},
author = {Qingtao Tang; Li Niu; Tao Dai; Xi Xiao; Shu-Tao Xia },
publisher = {IEEE SigPort},
title = {Self-paced mixture of t distribution model},
year = {2018} }
TY - EJOUR
T1 - Self-paced mixture of t distribution model
AU - Qingtao Tang; Li Niu; Tao Dai; Xi Xiao; Shu-Tao Xia
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3210
ER -
Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia. (2018). Self-paced mixture of t distribution model. IEEE SigPort. http://sigport.org/3210
Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia, 2018. Self-paced mixture of t distribution model. Available at: http://sigport.org/3210.
Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia. (2018). "Self-paced mixture of t distribution model." Web.
1. Qingtao Tang, Li Niu, Tao Dai, Xi Xiao, Shu-Tao Xia. Self-paced mixture of t distribution model [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3210