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The Lasso (L1-penalized regression) has drawn great interests in machine learning and statistics due to its robustness and high accuracy. A variety of methods have been proposed for solving the Lasso. But for large scale problems, the presence of L1 norm constraint significantly impedes the efficiency. Inspired by recent theoretical and practical contributions on the close relation between Lasso and...
This paper proposes a novel algorithm for blind watermarking by applying singular value decomposition and least squares support vector machine into watermark embedding and detection. In coding process, singular value decomposition is performed on coefficient blocks to obtain singular values after host image is transformed into integer wavelet transform domain. Subsequently, watermark image is embedded...
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