Pattern classification based on k locally constrained line

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.