Titlebook: Image Understanding using Sparse Representations; Jayaraman J. Tiagarajan,Karthikeyan Natesan Ramamu Book 2014 Springer Nature Switzerland

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Jayaraman J. Tiagarajan,Karthikeyan Natesan Ramamurthy,Pavan Turaga,Andreas Spanias macroeconomic policies.Includes supplementary material: This book is a quarterly forecast and analysis report on the Chinese economy. It is published twice a year and presents ongoing result from the “China Quarterly Macroeconomic Model (CQMM),” a research project at the Center for Macroeconomic Re

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Jayaraman J. Tiagarajan,Karthikeyan Natesan Ramamurthy,Pavan Turaga,Andreas Spanias macroeconomic policies.Includes supplementary material: This book is a quarterly forecast and analysis report on the Chinese economy. It is published twice a year and presents ongoing results from the “China Quarterly Macroeconomic Model (CQMM),” a research project at the Center for Macroeconomic R

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Sparse Representations, that represent the signals sparsely in a transform domain, such as the Fourier [41] or the wavelet domain [42], have been considered. Hence, sparse representation seeks to approximate an input signal by a linear combination of elementary signals. A common metric considered for measuring the sparsit

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Dictionary Learning: Theory and Algorithms,ble to tune the parameters of a pre-defined dictionary, such that the performance over the given set of data is optimized. However, dictionaries that are learned directly from the data result in an improved performance compared to both pre-defined as well as tuned dictionaries. This chapter will foc

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Sparse Models in Recognition,ding has been very effective in modeling natural signals, its ability to discriminate different classes of data is not inherent. Since sparse coding algorithms aim to reduce only the reconstruction error, they do not explicitly consider the correlation between the codes, which is crucial for classif

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