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A Biresolution Spectral Framework for Product Quantization
In this paper, we study the quantization problem in the setting where subspaces are orthogonal and show that this problem is intricately related to a specific type of spectral decomposition of the data.
Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging
Multiple Kernel Learning (MKL) generalizes SVMs to the setting where one simultaneously trains a linear classifier and chooses an optimal combination of given base kernels.
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