GLRAMGeneralized Low Rank Approximations of Matrices
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However, as bilateral projection based 2D feature extraction techniques, 2DMMC, 2DLDA, and GLRAM share such shortcomings: The iterations and alternations are time-consuming, and an arbitrary initial value of V cannot guarantee the global optimum.
Bilateral projection based 2D feature extraction techniques, such as 2DMMC, 2DLDA, and GLRAM, consider seeking transforms on both sides of image matrices, that is, both left and right projections are taken, but the computation of two-side projection matrices involves time-consuming iterations and alternations, and the initialization before iterations may lead to local optimum.
In this section, to investigate the performance of the proposed B2D-MMC for face recognition, we compare our method with PCA [1], LDA [2], MMC [3], GLRAM [6], 2DLDA [7], and 2DMMC [8], in both accuracy and efficiency.
Tables 1 and 2 show the experimental results of the proposed B2D-MMC on the two databases, respectively, with the best results of PCA, LDA, MMC, GLRAM, 2DLDA, and 2DMMC referred from [8] for comparison.