A multilinear unsupervised discriminant projections method for feature extraction View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-02

AUTHORS

Haiyan Chen, Chengshan Qian, Hao Zheng, Huan Wang

ABSTRACT

Despite considering the distribution information of data, unsupervised discriminant projection (UDP) ignores the space structure information of data for high order tensor objects. To address these problems, many tensor methods are developed for charactering the space structure information. Albeit effective, these methods ignore the local manifold structure of the samples, and thus achieve sub-optimal performance. In this paper, we formulate UDP in a high order tensor space and develop a Multilinear UDP (MUDP) for feature extraction on tensor objects. MUDP inherits the merits of UDP and Tensor based methods. The experiments tell that MUDP is an efficient and effective method and works well. More... »

PAGES

3857-3870

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11042-016-4243-z

DOI

http://dx.doi.org/10.1007/s11042-016-4243-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028871067


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