Publisher's Synopsis
Computational Statistics and Machine Learning: A Sparse Approach focuses on using sparse algorithms in statistics and machine learning. The first part addresses the L—0 norm minimization using greedy algorithms and considers the set covering machines, matching pursuit algorithms in machine learning, and random projection methods. The second part, which addresses L—1 norm minimization, discusses linear programming boosting, LASSO/LARS, and compressed sensing. All chapters include a detailed description of algorithms and pseudo–code and, where appropriate, a theoretical analysis of generalization ability motivating the use of sparsity. A final chapter covers applications.
