Machine learning based on sparsity-inducing regularization for matrices

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The outcomes of this research project can be summarized as follows:1. I have extended the dual augmented Lagrangian (DAL) algorithm to deal with spectral regularization for matrices and proposed the M-DAL algorithm(ICML2010). The super-linear convergence of DAL and M-DAL algorithms wasproven and published in JMLR. A review of DAL and related algorithms has beenpublished as part of “Optimization for Machine Learning” (MIT Press). I have alsomade the code publicly available to promote its use in wider research communities. 2. In order to deal with non-numerical data, I have extended DAL to handle multiplekernel learning with thousands of kernels. This was published in MachineLearning Journal. 3. I have extended the framework to spectral regularization for higher-order tensors and analyzed its statistical performance. This was presented at NIPS2011.