TY - GEN
T1 - Semi-supervised hierarchy learning using multiple-labeled data
AU - Javadi, Ailar
AU - Gray, Alexander
AU - Anderson, David
AU - Berisha, Visar
PY - 2011
Y1 - 2011
N2 - While hierarchical semi-supervised classification methods have been previously studied, we still lack an algorithm that can learn a non-predefined categorical hierarchy from multi-labeled data at various levels of specificity. Inspired by human psychology and learning experience, in this paper we propose a semi-supervised learning method that can classify multi-labeled data into a hierarchy based on the label's specificity level such that the separability between each class and its siblings is greater than the separability between each class and its parents. To build the hierarchy we show that a minimum spanning tree minimizes an upper bound on the pairwise Kullback-Liebler divergence between the true and approximated distributions. We show the effectiveness of our method using three types of data sets and draw a comparison between our learned hierarchy and one learned by human subjects using the same data set. We also show the effectiveness of our method compared to hierarchical clustering.
AB - While hierarchical semi-supervised classification methods have been previously studied, we still lack an algorithm that can learn a non-predefined categorical hierarchy from multi-labeled data at various levels of specificity. Inspired by human psychology and learning experience, in this paper we propose a semi-supervised learning method that can classify multi-labeled data into a hierarchy based on the label's specificity level such that the separability between each class and its siblings is greater than the separability between each class and its parents. To build the hierarchy we show that a minimum spanning tree minimizes an upper bound on the pairwise Kullback-Liebler divergence between the true and approximated distributions. We show the effectiveness of our method using three types of data sets and draw a comparison between our learned hierarchy and one learned by human subjects using the same data set. We also show the effectiveness of our method compared to hierarchical clustering.
UR - http://www.scopus.com/inward/record.url?scp=82455198876&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=82455198876&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2011.6064565
DO - 10.1109/MLSP.2011.6064565
M3 - Conference contribution
AN - SCOPUS:82455198876
SN - 9781457716232
T3 - IEEE International Workshop on Machine Learning for Signal Processing
BT - 2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011
T2 - 21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
Y2 - 18 September 2011 through 21 September 2011
ER -