Deep Correlation Structure Preserved Label Space Embedding for Multi-label Classification

Kaixiang Wang, Ming Yang, Wanqi Yang, YiLong Yin
Proceedings of The 10th Asian Conference on Machine Learning, PMLR 95:1-16, 2018.

Abstract

Label embedding is an effective and efficient method which can jointly extract the information of all labels for better performance of multi-label classification. However, most existing embedding methods ignore information of feature space or intrinsic structure of previous label space, such that their learned latent space will not have strong predictability and discriminant ability. We propose a novel deep neural network (DNN) based model, namely Deep Correlation Structure Preserved Label Space Embedding (DCSPE). Specifically, DCSPE derives a deep latent space by performing feature-aware label space embedding with deep canonical correlation analysis (DCCA) and preserving the intrinsic structure of the previous label space with proposed deep multidimensional scaling (DMDS). Our DCSPE is achieved by integrating the DNN architectures of the two DNN based models and can learn a feature-aware structure preserved deep latent space. Furthermore, extensive experimental results on datasets with many labels demonstrate that our proposed approach is significantly better than the existing label embedding algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v95-wang18a, title = {Deep Correlation Structure Preserved Label Space Embedding for Multi-label Classification}, author = {Wang, Kaixiang and Yang, Ming and Yang, Wanqi and Yin, YiLong}, booktitle = {Proceedings of The 10th Asian Conference on Machine Learning}, pages = {1--16}, year = {2018}, editor = {Zhu, Jun and Takeuchi, Ichiro}, volume = {95}, series = {Proceedings of Machine Learning Research}, month = {14--16 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v95/wang18a/wang18a.pdf}, url = {https://proceedings.mlr.press/v95/wang18a.html}, abstract = {Label embedding is an effective and efficient method which can jointly extract the information of all labels for better performance of multi-label classification. However, most existing embedding methods ignore information of feature space or intrinsic structure of previous label space, such that their learned latent space will not have strong predictability and discriminant ability. We propose a novel deep neural network (DNN) based model, namely Deep Correlation Structure Preserved Label Space Embedding (DCSPE). Specifically, DCSPE derives a deep latent space by performing feature-aware label space embedding with deep canonical correlation analysis (DCCA) and preserving the intrinsic structure of the previous label space with proposed deep multidimensional scaling (DMDS). Our DCSPE is achieved by integrating the DNN architectures of the two DNN based models and can learn a feature-aware structure preserved deep latent space. Furthermore, extensive experimental results on datasets with many labels demonstrate that our proposed approach is significantly better than the existing label embedding algorithms.} }
Endnote
%0 Conference Paper %T Deep Correlation Structure Preserved Label Space Embedding for Multi-label Classification %A Kaixiang Wang %A Ming Yang %A Wanqi Yang %A YiLong Yin %B Proceedings of The 10th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jun Zhu %E Ichiro Takeuchi %F pmlr-v95-wang18a %I PMLR %P 1--16 %U https://proceedings.mlr.press/v95/wang18a.html %V 95 %X Label embedding is an effective and efficient method which can jointly extract the information of all labels for better performance of multi-label classification. However, most existing embedding methods ignore information of feature space or intrinsic structure of previous label space, such that their learned latent space will not have strong predictability and discriminant ability. We propose a novel deep neural network (DNN) based model, namely Deep Correlation Structure Preserved Label Space Embedding (DCSPE). Specifically, DCSPE derives a deep latent space by performing feature-aware label space embedding with deep canonical correlation analysis (DCCA) and preserving the intrinsic structure of the previous label space with proposed deep multidimensional scaling (DMDS). Our DCSPE is achieved by integrating the DNN architectures of the two DNN based models and can learn a feature-aware structure preserved deep latent space. Furthermore, extensive experimental results on datasets with many labels demonstrate that our proposed approach is significantly better than the existing label embedding algorithms.
APA
Wang, K., Yang, M., Yang, W. & Yin, Y.. (2018). Deep Correlation Structure Preserved Label Space Embedding for Multi-label Classification. Proceedings of The 10th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 95:1-16 Available from https://proceedings.mlr.press/v95/wang18a.html.

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