TY - GEN
T1 - Learning from Label and Feature Heterogeneity
AU - Yang, Pei
AU - He, Jingrui
AU - Yang, Hongxia
AU - Fu, Haoda
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12
Y1 - 2014/12
N2 - Multiple types of heterogeneity, such as label heterogeneity and feature heterogeneity, often co-exist in many real-world data mining applications, such as news article categorization, gene functionality prediction. To effectively leverage such heterogeneity, in this paper, we propose a novel graph-based framework for Learning with both Label and Feature heterogeneities, namely L2F. It models the label correlation by requiring that any two label-specific classifiers behave similarly on the same views if the associated labels are similar, and imposes the view consistency by requiring that view-based classifiers generate similar predictions on the same examples. To solve the resulting optimization problem, we propose an iterative algorithm, which is guaranteed to converge to the global optimum. Furthermore, we analyze its generalization performance based on Rademacher complexity, which sheds light on the benefits of jointly modeling the label and feature heterogeneity. Experimental results on various data sets show the effectiveness of the proposed approach.
AB - Multiple types of heterogeneity, such as label heterogeneity and feature heterogeneity, often co-exist in many real-world data mining applications, such as news article categorization, gene functionality prediction. To effectively leverage such heterogeneity, in this paper, we propose a novel graph-based framework for Learning with both Label and Feature heterogeneities, namely L2F. It models the label correlation by requiring that any two label-specific classifiers behave similarly on the same views if the associated labels are similar, and imposes the view consistency by requiring that view-based classifiers generate similar predictions on the same examples. To solve the resulting optimization problem, we propose an iterative algorithm, which is guaranteed to converge to the global optimum. Furthermore, we analyze its generalization performance based on Rademacher complexity, which sheds light on the benefits of jointly modeling the label and feature heterogeneity. Experimental results on various data sets show the effectiveness of the proposed approach.
KW - heterogeneity
KW - multi-label learning
KW - multi-view learning
KW - Rademacher complexity
UR - https://www.scopus.com/pages/publications/84936939601
U2 - 10.1109/ICDM.2014.42
DO - 10.1109/ICDM.2014.42
M3 - Conference article published in proceeding or book
AN - SCOPUS:84936939601
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1079
EP - 1084
BT - Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
A2 - Kumar, Ravi
A2 - Toivonen, Hannu
A2 - Pei, Jian
A2 - Zhexue Huang, Joshua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Data Mining, ICDM 2014
Y2 - 14 December 2014 through 17 December 2014
ER -