Probability-Based Locally Linear Embedding for Classification

Authors:
Zhenyue Zhang;Lingxiao Zhao
Affiliations:
Zhejiang university, China;Zhejiang university, China
Venue:
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Year:
2007

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Abstract

We propose a novel dimension reduction method for clas- sification using a probability-based distance and the tech- nique of locally linear embedding (LLE). Logistic Discrim- ination (LD) is adopted for estimating the probability dis- tribution as well as for classification for the reduced data. Different to the supervised locally linear embedding (SLLE) that is only used for the dimension reduction of train- ing data, our probability-based locally linear embedding (PLLE) can be applied on both training and testing data. Five microarray data sets in high dimensional spaces, the IRIS data, and a real set of handwritten digits are experi- mented. The numerical results show that our method per- forms better, compared with the LD classifiers applied on the LLE or SLLE mapped lower dimensional data.