Titlebook: Cognitive Networked Sensing and Big Data; Robert Qiu,Michael Wicks Book 2014 The Editor(s) (if applicable) and The Author(s), under exclus

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容易生皱纹

Proponent

Pde5-Inhibitors

Concentration of Measuretration inequalities are often used to investigate the sums of random variables (scalars, vectors and matrices). In particular, we survey the recent status of sums of random matrices in Chap. 2, which gives us the straightforward impression of the classical view of the subject.

affect

Concentration of Eigenvalues and Their Functionalse the mathematics objects. Eigenvalues and their functionals may be shown to be Lipschitz functions so the Talagrand’s framework is sufficient. Concentration inequalities for many complicated random variables are also surveyed here from the latest publications. As a whole, we bring together concentr

Critical

Non-asymptotic, Local Theory of Random Matricese in the sense of random matrices. The point of viewing this chapter as a novel statistical tool will have far-reaching impact on applications such as covariance matrix estimation, detection, compressed sensing, low-rank matrix recovery, etc. Two primary examples are: (1) approximation of covariance

Surgeon

adumbrate

Matrix Completion and Low-Rank Matrix Recoveryssed sensing exploits the sparsity structure in a vector, while low-rank matrix recovery—Chap. 8—exploits the low-rank structure of a matrix: sparse in the vector composed of singular values. The theory ultimately traces back to concentration of measure due to high dimensions.

Painstaking

Covariance Matrix Estimation in High Dimensionser should be more basic than Chaps. 7 and 8—thus should be treated earlier chapters. Recent work on compressed sensing and low-rank matrix recovery supports the idea that sparsity can be exploited for statistical estimation, too. The treatment of this subject is very superficial, due to the limited

蚊子

Database Friendly Data Processingence of sensing, computing, networking and control. Data base is often neglected in traditional treatments in estimation, detection, etc..Modern scientific computing demands efficient algorithms for dealing with large datasets—Big Data. Often these datasets can be fruitfully represented and manipula