Deep Learning Through Sparse And Low-rank Modeling

Deep Learning Through Sparse Representation And Low-rank Modeling Bridges Classical Sparse And Low Rank Models-those That Emphasize Problem-specific Interpretability-with Recent Deep Network Models That Have Enabled A Larger Learning Capacity And Better Utilization Of Big Data. It Shows How The Toolkit Of Deep Learning Is Closely Tied With The Sparselow Rank Methods And Algorithms, Providing A Rich Variety Of Theoretical And Analytic Tools To Guide The Design And Interpretation Of Deep Learning Models. The Development Of The Theory And Models Is Supported By A Wide Variety Of Applications In Computer Vision, Machine Learning, Signal Processing, And Data Mining.