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Showing 1 to 8 of 8 for “"K-SVD"”.

  1. GDSVD: Scalable k-SVD via Gradient Descent

    … rule for step-size selection provably finds k-SVD, i.e., the k ≥ 1 largest singular values and corresponding vectors, of any matrix, despite nonconvexity. There has been substantial progress towards this in the past few years where existing results are able to establish such guarantees for the …

    mit Repository record for GDSVD: Scalable k-SVD via Gradient Descent (opens in a new tab)

  2. Určení optimální velikosti bloků pro řídkou reprezentaci obrazu

    … bloku pro extrapolaci pomocí algoritmu K-SVD.

    brno-tech Repository record for Určení optimální velikosti bloků pro řídkou reprezentaci obrazu (opens in a new tab)

  3. Computation tools for the Fourier transform infrared (FT-IR) spectroscopic imaging

    … PSF. For sparse reconstruction, we train a K-SVD dictionary to sparsely represent the interferograms. Then, using optimization algorithms, we recover the full dimensional interferograms from very few measurements. Using experimental results on the standard United States Air Force (USAF) 1951 …

    uiuc Repository record for Computation tools for the Fourier transform infrared (FT-IR) spectroscopic imaging (opens in a new tab)

  4. Reduced order modeling for stochastic prediction and data assimilation onboard autonomous platforms at sea

    … using schemes such as POD projection and K-SVD for sparse representation due to showing promise for distributing forecasts efficiently to remote vehicles. In the second part, we combine DMD methods with the GMM-DO filter to produce DMD forecasts with Bayesian data assimilation that can …

    woods-hole Repository record for Reduced order modeling for stochastic prediction and data assimilation onboard autonomous platforms at sea (opens in a new tab)

  5. Reduced Order Modeling for Stochastic Prediction and Data Assimilation Onboard Autonomous Platforms At Sea

    … using schemes such as POD projection and K-SVD for sparse representation due to showing promise for distributing forecasts efficiently to remote vehicles. In the second part, we combine DMD methods with the GMM-DO filter to produce DMD forecasts with Bayesian data assimilation that can …

    mit Repository record for Reduced Order Modeling for Stochastic Prediction and Data Assimilation Onboard Autonomous Platforms At Sea (opens in a new tab)

  6. Compressed Sensing Techniques for EEG Signals

    auckland-tech

  7. Dictionary learning for scalable sparse image representation

    … algorithm is built upon the foundation of the K-SVD framework originally designed to learn non-scalable dictionaries for natural images. The scalable dictionary learning design is mainly motivated by the main perception characteristics of the Human Visual System (HVS) mechanism. Specifically, its …

    strathclyde Repository record for Dictionary learning for scalable sparse image representation (opens in a new tab)

  8. Adaptive sparse representations and their applications

    … learned synthesis dictionaries such as the K-SVD algorithm. In the third part of this thesis, we further develop the alternating algorithms for learning unstructured (non-sparse) well-conditioned, or orthonormal square sparsifying transforms. While, in the first part of the thesis, we provided …

    uiuc Repository record for Adaptive sparse representations and their applications (opens in a new tab)