TY - GEN
T1 - Robust Adaptive-weighting Multi-view Classification
AU - Jiang, Bingbing
AU - Xiang, Junhao
AU - Wu, Xingyu
AU - He, Wenda
AU - Hong, Libin
AU - Sheng, Weiguo
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/10/26
Y1 - 2021/10/26
N2 - As data sources become ever more numerous, classification for multi-view data represented by heterogeneous features has been involved in many data mining applications. Most existing methods either directly concatenate all views or separately tackle each view, neglecting the correlation and diversity among views. Moreover, they often encounter an extra hyper-parameter that needs to be manually tuned, degenerating the applicability of models. In this paper, we present a robust supervised learning framework for multi-view classification, seeking a better representation and fusion of multiple views. Specifically, our framework discriminates different views with adaptively optimized view-wise weight factors and coalesces them to learn a joint projection subspace compatible across multiple views in an adaptive-weighting manner, thereby avoiding the intractable hyper-parameter. Meanwhile, the consensus and complementary information of original views can be naturally integrated into the learned subspace, in turn enhancing the discrimination of the subspace for subsequent classification. An efficient convergent algorithm is developed to iteratively optimize the formulated framework. Experiments on real datasets demonstrate the effectiveness and superiority of the proposed method.
AB - As data sources become ever more numerous, classification for multi-view data represented by heterogeneous features has been involved in many data mining applications. Most existing methods either directly concatenate all views or separately tackle each view, neglecting the correlation and diversity among views. Moreover, they often encounter an extra hyper-parameter that needs to be manually tuned, degenerating the applicability of models. In this paper, we present a robust supervised learning framework for multi-view classification, seeking a better representation and fusion of multiple views. Specifically, our framework discriminates different views with adaptively optimized view-wise weight factors and coalesces them to learn a joint projection subspace compatible across multiple views in an adaptive-weighting manner, thereby avoiding the intractable hyper-parameter. Meanwhile, the consensus and complementary information of original views can be naturally integrated into the learned subspace, in turn enhancing the discrimination of the subspace for subsequent classification. An efficient convergent algorithm is developed to iteratively optimize the formulated framework. Experiments on real datasets demonstrate the effectiveness and superiority of the proposed method.
KW - multi-view learning
KW - supervised classification
UR - http://www.scopus.com/inward/record.url?scp=85119174971&partnerID=8YFLogxK
U2 - 10.1145/3459637.3482173
DO - 10.1145/3459637.3482173
M3 - Conference contribution
AN - SCOPUS:85119174971
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 3117
EP - 3121
BT - CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 30th ACM International Conference on Information and Knowledge Management, CIKM 2021
Y2 - 1 November 2021 through 5 November 2021
ER -