Python implementation of sparse dictionary learning. Trains on Van Hateran's or David Field's natural image datasets. Based upon Sparsenet algorithm (1996 Olshausen & Field). Standard stochastic gradient descent to learn dictionary and multiple algorithms to infer coefficients. Minimizes the following ...
In sparse dictionary learning-based face recognition, $$l_1$$-based sparse representation (SR) and SVD-based dictionary learning (DL) both have shown promising performance. How to utilize both of them in an enhanced training process by data augmentation is still unclear. This paper proposes a ...
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Next, we briefly introduce the sparse graph-regularized dictionary learning (SGRDL) regularizer for solving the FWI problem. Then, the propose SGRDL-FWI method and share the corresponding numerical algorithm. Finally, we present series of experiments that demonstrate the superiority of our SGRDL-FWI...
In this paper, we propose a novel joint graph regularized dictionary learning and sparse ranking method for multi-modal multi-shot person Re-ID. First, we explore the probe-based geometrical structure by enforcing the smoothness between the codings/coefficients, which refers to the multi-shot ...
We propose a flexible computational method scParser (sparserepresentation learning forscalable single-cellRNA sequencing data analysis), presented in Fig.1. Here we use donors and phenotypes (e.g., disease status) as an example of biological conditions. The RNA expression profiles of cells from di...
python sparsity optimization cuda admm sparse-coding dictionary-learning optimization-algorithms robust-pca fista convolutional-sparse-coding total-variation sparse-representations convolutional-dictionary-learning total-variation-minimization plug-and-play-priors Updated Apr 29, 2024 Python alphacsc / alphacsc...
"""Private method allowing to accommodate both DictionaryLearning and SparseCoder.""" code = check_array(code) # compute number of expected features in code expected_n_components = dictionary.shape[0] if self.split_sign: expected_n_components += expected_n_components if not code.shape[1] =...
our proposed dictionary learning scheme is theoretically guaranteed to converge to the set of stationary points under certain mild assumptions. For the image denoising application, the performance and the efficiency of the proposed dictionary learning scheme are comparable to that of K-SVD algorithm in...
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