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

facebooktwittermailshare

An Empirical Bayes Approach to Partially Labeled and Shuffled Data Sets

Abstract: 

This work outlines a method for an application of empirical Bayes in the setting of semi-supervised learning. That is, we consider a scenario in which the training set is partially or entirely unlabeled. In addition to the missing labels, we also consider a scenario where the available training data might be shuffled (i.e., the features and labels are not matched).

Specifically, we propose to train model-based empirical Bayes separately on the set of features and the set of labels and combine/mix the two models based on the proportion of unlabeled pairs. The method then can be used to recover the missing labels (i.e., create pseudo-labels) of the data set and, in addition, if the data is shuffled, recover the correct permutation of the data. The technique is evaluated for a multivariate Gaussian model and is shown to consistently outperform a maximum likelihood approach. Moreover, the procedure is shown to be a consistent estimator for a multivariate Gaussian model with an arbitrary (non-degenerate) covariance matrix.

up
0 users have voted:

Comments

n/A

Paper Details

Authors:
Alex Dytso, H. Vincent Poor
Submitted On:
13 February 2020 - 3:22pm
Short Link:
Type:
Research Manuscript
Event:
Presenter's Name:
Alex Dytso
Paper Code:
2692
Document Year:
2020
Cite

Document Files

ICASSP.pdf

(68)

Keywords

Additional Categories

Subscribe

[1] Alex Dytso, H. Vincent Poor, "An Empirical Bayes Approach to Partially Labeled and Shuffled Data Sets", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4989. Accessed: Jul. 07, 2020.
@article{4989-20,
url = {http://sigport.org/4989},
author = {Alex Dytso; H. Vincent Poor },
publisher = {IEEE SigPort},
title = {An Empirical Bayes Approach to Partially Labeled and Shuffled Data Sets},
year = {2020} }
TY - EJOUR
T1 - An Empirical Bayes Approach to Partially Labeled and Shuffled Data Sets
AU - Alex Dytso; H. Vincent Poor
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4989
ER -
Alex Dytso, H. Vincent Poor. (2020). An Empirical Bayes Approach to Partially Labeled and Shuffled Data Sets. IEEE SigPort. http://sigport.org/4989
Alex Dytso, H. Vincent Poor, 2020. An Empirical Bayes Approach to Partially Labeled and Shuffled Data Sets. Available at: http://sigport.org/4989.
Alex Dytso, H. Vincent Poor. (2020). "An Empirical Bayes Approach to Partially Labeled and Shuffled Data Sets." Web.
1. Alex Dytso, H. Vincent Poor. An Empirical Bayes Approach to Partially Labeled and Shuffled Data Sets [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4989