Supervised discriminative manifold learning with subsidiary-view information for near infrared spectroscopic classification of crop seeds

摘要

This paper introduces a novel manifold learning method for near infrared spectroscopic classification dimensionality reduction and feature extraction. First, similar spectra from different categories reduced by traditional methods are mixed seriously which becomes an obstacle to build an effective model. We present a novel application to discovering the spectral manifold structure through manifold learning. Second, we propose a new supervised discriminative manifold learning method to expanded category overlapping for dimensionality reduction in classification and pattern recognition. The proposed method constructs a feature space which can make the samples of different categories in overlapping region far away from each other and keep the samples of same category in non-overlapping region close to each other. Accordingly, the low-dimensional feature space with expanding the overlap region is more conducive to classification. Moreover, the subsidiary-view version of the proposed method is proposed to further improve the classification accuracy. Finally, an authentic spectra dataset obtained from 200 maize seeds is introduced for building a haploid identification model to verify the method. Experimental results show that the proposed method outperforms several relevant manifold learning method and common dimensionality reduction methods of spectra. Furthermore, it is validated that subsidiary-view manifold learning is better than single view under appropriate parameters in near infrared spectral dimensionality reduction and classification. (C) 2019 Published by Elsevier B.V.