The sensitivity of recovery algorithms with respect to a perfect knowledge of the encoding matrix is a general issue in many application scenarios in which compressed sensing is an option to acquire or encode natural ...
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ISBN:
(纸本)9781467369985
The sensitivity of recovery algorithms with respect to a perfect knowledge of the encoding matrix is a general issue in many application scenarios in which compressed sensing is an option to acquire or encode natural signals. Quantifying this sensitivity in order to predict the result of signal recovery is therefore valuable when no a priori information can be exploited, e.g., when the encoding matrix is randomly perturbed without any exploitable structure. We tackle this aspect by means of a simplified model for the signal recovery problem, which enables the derivation of an average performance estimate that depends only on the interaction between the sensing and perturbation matrices. The effectiveness of the resulting heuristic is demonstrated by numerical exploration of signal recovery under three simple perturbation matrix models. Finally, we show how this estimate matches very well the degradation experienced by non-perfectly informed decoders in applications of compressed sensing to protecting the acquired information content in ECG tracks and sensitive images.
We theoretically analyze and compare the following five popular multiclass classification methods: One vs. All, All Pairs, Tree-based classifiers, Error Correcting Output Codes (ECOC) with randomly generated code matr...
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ISBN:
(纸本)9781627480031
We theoretically analyze and compare the following five popular multiclass classification methods: One vs. All, All Pairs, Tree-based classifiers, Error Correcting Output Codes (ECOC) with randomly generated code matrices, and Multiclass SVM. In the first four methods, the classification is based on a reduction to binary classification. We consider the case where the binary classifier comes from a class of VC dimension d, and in particular from the class of half spaces over R~d. We analyze both the estimation error and the approximation error of these methods. Our analysis reveals interesting conclusions of practical relevance, regarding the success of the different approaches under various conditions. Our proof technique employs tools from VC theory to analyze the approximation error of hypothesis classes. This is in contrast to most previous uses of VC theory, which only deal with estimation error.
An adaptive distributed space-time coding (DSTC) scheme is proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receive filters and adjustable matrices subject to a power constraint ...
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ISBN:
(纸本)9781467363365
An adaptive distributed space-time coding (DSTC) scheme is proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receive filters and adjustable matrices subject to a power constraint are considered with an amplify-and-forward (AF) cooperation strategy. In the proposed DSTC scheme, an adjustable matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. Linear MMSE expressions of the adjustable code matrices based on the mean square error (MSE) and the maximum likelihood (ML) criteria are derived. The effects of the limited feedback and the feedback errors on the performance are considered. A stochastic gradient (SG) algorithm is also developed with reduced computational complexity. The simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.
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