Why Sparse? Fuzzy Techniques Explain Empirical Efficiency of Sparsity-Based Data- and Image-Processing Algorithms

Cervantes, Fernando; Usevitch, Bryan; Valera, Leobardo; Kreinovich, Vladik

doi:10.1007/978-3-319-75408-6_32

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Why Sparse? Fuzzy Techniques Explain Empirical Efficiency of Sparsity-Based Data- and Image-Processing Algorithms

Cited by 3 publications

References 18 publications

Applications to Computing: Why Deep Learning is More Efficient Than Support Vector Machines, and How It is Related to Sparsity Techniques in Signal Processing

Applications to Computing: Why Deep Learning is More Efficient Than Support Vector Machines, and How It is Related to Sparsity Techniques in Signal Processing

Rotation-invariance can further improve state-of-the-art blind deconvolution techniques

Why Deep Learning Is More Efficient than Support Vector Machines, and How it is Related to Sparsity Techniques in Signal Processing

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