Abstract
A simple yet effective learning algorithm, k locally constrained line (k-LCL), is presented for pattern classification. In k-LCL, any two prototypes of the same class are extended to a constrained line (CL), through which the representational capacity of the training set is largely improved. Because each CL is adjustable in length, k-LCL can well avoid the “intersecting” of training subspaces in most traditional feature classifiers. Moreover, to speed up the calculation, k-LCL classifies an unknown sample focusing only on its local CLs in each class. Experimental results, obtained on both synthetic and real-world benchmark data sets, show that the proposed method has better accuracy and efficiency than most existing feature line methods.
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Acknowledgments
The authors would like to thank the anonymous referees as well as the guest editors for their helpful comments and suggestions. This research has been supported by the National Key Basic Research and Development Program of China (Grant No. 2006CB701303) and the National High Technology Research and Development Program of China (Grant No. 2006AA12Z105).
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Qing, J., Huo, H. & Fang, T. Pattern classification based on k locally constrained line. Soft Comput 15, 703–712 (2010). https://doi.org/10.1007/s00500-010-0602-2
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DOI: https://doi.org/10.1007/s00500-010-0602-2