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Angle-based embedding quality assessment method for manifold learning

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Abstract

Manifold learning (ML) is a research topic of great interest in the field of machine learning that aims to determine the appropriate low-dimensional embeddings of data. The embeddings should preserve the intrinsic structure of the data manifold. Many ML techniques have been proposed to learn the underlying manifold of data. It is crucial to effectively evaluate the quality of the corresponding embedding results when selecting an appropriate ML technique in practice. However, there is a lack of effective embedding quality assessment (EQA) criteria to evaluate the embedding quality. In this paper, a new local included angles preservation (LUNA) criterion is proposed to evaluate the embedding quality. Unlike previous EQA methods that mainly focus on local neighborhood preservation performance or the preservation of the global geometric structure, the proposed LUNA criterion incorporates both an assessment of the neighborhood preserving capacity and local included angle holding performance. By introducing an effective evaluation of the performance of local included angle preservation, we show that the LUNA criterion could provide a more reasonable quality assessment than conventional criteria. The implementation of the LUNA criterion is direct and simple. To the best of our knowledge, this is the first EQA method that explicitly takes into account the preservation of local included angles. The effectiveness of the LUNA criterion is experimentally supported by its outstanding performance on a series of benchmark datasets.

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Acknowledgements

This work was supported by the National Science Foundation of China (Grant Nos. 61375065 and 61625204), partially supported by the State Key Program of National Science Foundation of China (Grant Nos. 61432012 and 61432014).

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Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of China

    Dongdong Chen, Jiancheng Lv, Jing Yin & Haixian Zhang

  2. Chengdu University of Information Technology, Chengdu, 610000, People’s Republic of China

    Xiaojie Li

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  1. Dongdong Chen
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  2. Jiancheng Lv
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Correspondence to Jiancheng Lv.

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Chen, D., Lv, J., Yin, J. et al. Angle-based embedding quality assessment method for manifold learning. Neural Comput & Applic 31, 839–849 (2019). https://doi.org/10.1007/s00521-017-3113-6

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