Paper Info
Title
Capturing correlations of multiple labels: A generative probabilistic model for multi-label learning
Abstract
Recent years have witnessed a considerable surge of interest in the multi-label learning problem. It has been shown that a key factor for a successful multi-label learning algorithm is to effectively exploit relations between labels. However, most of the previous work exploiting label relations focuses on pairwise relations. To handle the situations where there are intrinsic correlations among multiple labels, in this paper, we propose a generative model, Labeled Four-Level Pachinko Allocation Model (L-F-L-PAM), to capture correlations among multiple labels. In our approach of multi-label learning on text data, we apply the proposed model for inferring the training data and the standard Four-Level Pachinko Allocation Model for the test data. Furthermore, we propose a pruned Gibbs Sampling algorithm in the test stage to reduce the inference time. Finally, extensive experiments have been performed to validate the effectiveness and efficiency of our new approach. The results demonstrate significant improvements of our model over Labeled LDA (L-LDA) and superiority in terms of both effectiveness and computational efficiency over other high-performing multi-label learning methods.
Year
DOI
Venue
2012
10.1016/j.neucom.2011.08.039
Neurocomputing
Keywords
Field
DocType
training data,multiple label,generative probabilistic model,successful multi-label,test data,text data,generative model,high-performing multi-label,multi-label learning,capturing correlation,allocation model,ranking
Data mining,Computer science,Pachinko allocation,Artificial intelligence,Gibbs sampling,Pairwise comparison,Pattern recognition,Ranking,Inference,Test data,Statistical model,Machine learning,Generative model
Journal
Volume
ISSN
Citations 
92,
0925-2312
11
PageRank 
References 
Authors
0.48
30
4
Name
Order
Citations
PageRank
Haiping Ma145023.63
Enhong Chen2123586.93
Linli Xu379042.51
Hui Xiong44958290.62