UDC 004.65, DOI: 10.2298/CSIS1001127Z

Effective Semi-supervised Nonlinear Dimensionality Reduction for Wood Defects Recognition

Zhao Zhang1 and Ning Ye1, 2

  1. School of Information Technology, Nanjing Forestry University
    210037 Nanjing, China
    zzhang618@gmail.com
  2. School of Computer Science and Technology, Shandong University
    250100 Jinan, China
    ye.ning@yahoo.com.cn

Abstract

Dimensionality reduction is an important preprocessing step in high-dimensional data analysis without losing intrinsic information. The problem of semi-supervised nonlinear dimensionality reduction called KNDR is considered for wood defects recognition. In this setting, domain knowledge in forms of pairs constraints are used to specify whether pairs of instances belong to the same class or different classes. KNDR can project the data onto a set of ‘useful’ features and preserve the structure of labeled and unlabeled data as well as the constraints defined in the embedding space, under which the projections of the original data can be effectively partitioned from each other. We demonstrate the practical usefulness of KNDR for data visualization and wood defects recognition through extensive experiments. Experimental results show it achieves similar or even higher performances than some existing methods.

Key words

semi-supervised learningm, dimensionality reduction, wood defects recognition, (dis-)similar constraints

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS1001127Z

Publication information

Volume 7, Issue 1 (February 2010)
Advances in Computer Animation and Digital Entertainment
Year of Publication: 2010
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Zhang, Z., Ye, N.: Effective Semi-supervised Nonlinear Dimensionality Reduction for Wood Defects Recognition. Computer Science and Information Systems, Vol. 7, No. 1. (2010)