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DKE-RLS: A Manifold Reconstruction Algorithm in Label Spaces with Double Kernel Embedding-Regularized Least Square

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PRICAI 2018: Trends in Artificial Intelligence (PRICAI 2018)

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Abstract

Dimension reduction is an important preprocess for multi-label classification, such as feature extraction. This paper attempts to explore multi-label learning in the label space. Our approach works using machine learning’s smoothness assumption, where nearby points are more likely to share the same label and the feature manifold and label manifold can share the local topology structure. Thus, here we propose a new multi-label feature-extraction algorithm with a new method for embedding regression, i.e., manifold regularization learning in the subspace formed by multi-labels to reconstruct and use the label manifold. We integrate two least-squares formulas by linear combination, and establish the regression estimation for multi-label manifold learning. To test our approach, we conduct multiple experiments and compare our algorithm against four other multi-label learning algorithms. Results show that our approach significantly improves the performance of label manifold learning.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (41471371, 61702270), the Project funded by China Postdoctoral Science Foundation under Grant. 2017M621592 and the open project foundation of Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Grant No. IIPL-2016-009.

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, China

    Chao Tan & Genlin Ji

Authors
  1. Chao Tan
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  2. Genlin Ji
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Correspondence to Chao Tan .

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Editors and Affiliations

  1. Southeast University, Nanjing, China

    Xin Geng

  2. University of Tasmania, Hobart, Tasmania, Australia

    Byeong-Ho Kang

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Tan, C., Ji, G. (2018). DKE-RLS: A Manifold Reconstruction Algorithm in Label Spaces with Double Kernel Embedding-Regularized Least Square. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-97304-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97303-6

  • Online ISBN: 978-3-319-97304-3

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