A.Victoria Anand Mary, S. Muthukumaran, S. Anand Christy
Uses of signal sparsity in a transform or dictionary domain include noise reduction, inverse problems, and compression. In contrast to analytical dictionary models, data-driven synthesis dictionary modification has recently shown potential. Dictionary learning problems, on the other hand, are typically NP-hard and non-convex, and the standard alternating minimization algorithms for these problems are computationally intensive, with the synthesis-sparse-encoding phase accounting for of most computations. This article examines in detail effective methods for learning dictionaries affected by aggregate sparsity. The data is first approximated with a sum of sparse rank one matrices (outer products) and then a block coordinate descent approach is used to estimate the unknowns. The article uses concepts underlying the algorithms, such as efficient closed-loop solutions involved in the block coordinate descent algorithms created. In addition, we address the issue of dictionary-blind image reconstruction and propose innovative and effective adaptive image reconstruction methods using sum of outer products and block coordinate descent approaches. We present a convergence study on dictionary-blind image reconstruction and dictionary learning algorithms. Our numerical tests demonstrate the promising performance and speedup over previous systems that the proposed methods offer in compressed sensor-based image reconstruction and sparse data representation.
Intrusion Detection, Dictionary, Cloud Security, Honey Spot Network.