Cloud K-SVD is a dictionary learning algorithm that can train at multiple nodes and hereby produce mutual to represent low-dimensional geometric structures in image data. We present novel application of the as we use it recover both noiseless noisy images from overlapping patches. implement node network Kubernetes using Docker containers facilitate K-SVD. Results show approximately remove quant...

We demonstrate an implementation for an approximate rank-k SVD factorization, combiningwell-known randomized projection techniques with previously implemented map/reduce solutions in order to compute steps of the random projection based SVD procedure, such QR and SVD. We structure the problem in a way that it reduces to Cholesky and SVD factorizations on k× k matrices computed on a single machi...

Most of the research on dictionary learning has focused on developing algorithms under the assumption that data is available at a centralized location. But often the data is not available at a centralized location due to practical constraints like data aggregation costs, privacy concerns, etc. Using centralized dictionary learning algorithms may not be the optimal choice in such settings. This ...

We develop a dictionary learning algorithm by minimizing the `1 distortion metric on the data term, which is known to be robust for non-Gaussian noise contamination. The proposed algorithm exploits the idea of iterative minimization of weighted `2 error. We refer to this algorithm as `1-K-SVD, where the dictionary atoms and the corresponding sparse coefficients are simultaneously updated to min...

Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we p...

Sparse representation has been widely used in machine learning, signal processing and communications. K-SVD, which generalizes k-means clustering, is one of the most famous algorithms for sparse representation and dictionary learning. K-SVD is an iterative method that alternates between encoding the data sparsely by using the current dictionary and updating the dictionary based on the sparsely ...

We propose a rank-k variant of the classical Frank-Wolfe algorithm to solve convex optimization over a trace-norm ball. Our algorithm replaces the top singular-vector computation (1-SVD) in Frank-Wolfe with a top-k singular-vector computation (k-SVD), which can be done by repeatedly applying 1-SVD k times. Our algorithm has a linear convergence rate when the objective function is smooth and str...

We develop a fast algorithm for computing the “SVD-truncated” regularized solution to the leastsquares problem: minx ‖Ax − b‖2. Let Ak of rank k be the best rank k matrix computed via the SVD of A. Then, the SVD-truncated regularized solution is: xk = A † k b. If A is m × n, then, it takes O(mnmin{m,n}) time to compute xk using the SVD of A. We give an approximation algorithm for xk which const...