Recently a new classification assumption was proposed in [1]. It assumed that the training sample... more Recently a new classification assumption was proposed in [1]. It assumed that the training samples of a particular class approximately form a linear basis for any test sample belonging to that class. The classification algorithm in [1] was based on the idea that all the correlated training samples belonging to the correct class are used to represent the test sample. The Lasso regularization was proposed to select the representative training samples from the entire training set (consisting of all the training samples). Lasso however ...
2005 International Conference on Wireless Networks, Communications and Mobile Computing, 2005
Abstract-In this work, we study the problem of end-to-end video transmission over a noisy communi... more Abstract-In this work, we study the problem of end-to-end video transmission over a noisy communication link, and analytically characterize the complexity (of motion search) and robustness metrics for this problem. The presence of channel noise results in an uncertainty at the encoder as to ...
This paper proposes a Majorization-Minimization approach for solving the synthesis and analysis p... more This paper proposes a Majorization-Minimization approach for solving the synthesis and analysis prior joint-sparse multiple measurement vector reconstruction problem. The proposed synthesis prior algorithm yielded the same results as the Spectral Projected Gradient (SPG) method. The analysis prior algorithm is the first to be proposed for this problem. It yielded considerably better results than the proposed synthesis prior algorithm. For problems of a given size, the run times for our proposed algorithms are fixed; unlike ...
Recently a new classification assumption was proposed in [1]. It assumed that the training sample... more Recently a new classification assumption was proposed in [1]. It assumed that the training samples of a particular class approximately form a linear basis for any test sample belonging to that class. The classification algorithm in [1] was based on the idea that all the correlated training samples belonging to the correct class are used to represent the test sample. The Lasso regularization was proposed to select the representative training samples from the entire training set (consisting of all the training samples). Lasso however ...
2005 International Conference on Wireless Networks, Communications and Mobile Computing, 2005
Abstract-In this work, we study the problem of end-to-end video transmission over a noisy communi... more Abstract-In this work, we study the problem of end-to-end video transmission over a noisy communication link, and analytically characterize the complexity (of motion search) and robustness metrics for this problem. The presence of channel noise results in an uncertainty at the encoder as to ...
This paper proposes a Majorization-Minimization approach for solving the synthesis and analysis p... more This paper proposes a Majorization-Minimization approach for solving the synthesis and analysis prior joint-sparse multiple measurement vector reconstruction problem. The proposed synthesis prior algorithm yielded the same results as the Spectral Projected Gradient (SPG) method. The analysis prior algorithm is the first to be proposed for this problem. It yielded considerably better results than the proposed synthesis prior algorithm. For problems of a given size, the run times for our proposed algorithms are fixed; unlike ...
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