Abstract
A new subspace analysis approach named ANLBM is proposed based on Laplacianfaces. It uses the discriminant information of training samples by supervised mechanism, enhances within-class local information by an objective function. The objective function is used to construct adjacency graph’s weight matrix. In order to avoid the drawback of Laplacianfaces’ PCA step, ANLBM uses kernel mapping. ANLBM changes the problem from minimum eigenvalue solution to maximum eigenvalue solution, reduces the redundancy of the computing and increases the precision of the result. The experiments are performed on ORL and Yale databases. Experimental results show that ANLBM has a better performance.
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Wu, Y., Gu, RM. (2006). A New Subspace Analysis Approach Based on Laplacianfaces. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_28
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DOI: https://doi.org/10.1007/11893257_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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