A new embedding quality assessment method for manifold learning

NEUROCOMPUTING

卷:97

Article

Zhang, Peng¹  Ren, Yuanyuan²  Zhang, Bo³ 

[1] Natl Disaster Reduct Ctr China, Ctr Data, Beijing, Peoples R China

[2] Tsinghua Univ, Career Ctr, Beijing 100084, Peoples R China

[3] Chinese Acad Sci, Inst Appl Math, Acad Math & Syst Sci, Beijing, Peoples R China

关键词: Nonlinear dimensionality reduction;    Manifold learning;    Data analysis;   

DOI  :  10.1016/j.neucom.2012.05.013

来源: Elsevier

【 摘 要 】

Manifold learning is a hot research topic in the field of computer science. A crucial issue with current manifold learning methods is that they lack a natural quantitative measure to assess the quality of learned embeddings, which greatly limits their applications to real-world problems. In this paper, a new embedding quality assessment method for manifold learning, named as normalization independent embedding quality assessment (NIEQA) is proposed. Compared with current assessment methods which are limited to isometric embeddings, the NIEQA method has a much larger application range due to two features. First, it is based on a new measure which can effectively evaluate how well local neighborhood geometry is preserved under normalization, hence it can be applied to both isometric and normalized embeddings. Second, it can provide both local and global evaluations to output an overall assessment. Therefore, NIEQA can serve as a natural tool in model selection and evaluation tasks for manifold learning. Experimental results on benchmark data sets validate the effectiveness of the proposed method. (C) 2012 Elsevier B.V. All rights reserved.

【 授权许可】

Free