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Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

In the era of big data, analysts usually explore various statistical models or machine-learning methods for observed data to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates. 

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

As part of the IEEE Signal Processing Society (SPS), the Speech and Language Technical Committee (SLTC) promotes research and development activities for technologies that are used to process speech and natural language.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

The Bio-Imaging and Signal Processing Technical Committee (BISP-TC) of the IEEE Signal Processing Society (SPS) promotes activities in the broad technical areas of computerized image and signal processing with a clear focus on applications in biology and medicine.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Extracting information from a signal exhibiting damped resonances is a challenging task in many practical cases due to the presence of noise and high attenuation. The interpretation of the signal relies on a model whose order (i.e., the number of resonances) is in general unknown.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

We generalize the 1-bit matrix completion problem to higher order tensors. Consider a rank- r order- d tensorT in RN ×⋯×RN  with bounded entries. We show that when r=O(1) , such a tensor can be estimated efficiently from only m=Or (Nd)  binary measurements. This shows that the sample complexity of recovering a low-rank tensor from 1-bit measurements of a subset of its entries is roughly the same as recovering it from unquantized measurements—a result that had been known only in the matrix case, i.e., when d=2.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

In this paper, four iterative algorithms for learning analysis operators are presented. They are built upon the same optimization principle underlying both Analysis K-SVD and Analysis SimCO. The forward and sequential analysis operator learning (AOL) algorithms are based on projected gradient descent with optimally chosen step size. The implicit AOL algorithm is inspired by the implicit Euler scheme for solving ordinary differential equations and does not require to choose a step size.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

The Advertisers Index contained in this issue is compiled as a service to our readers and advertisers: the publisher is not liable for errors or omissions although every effort is made to ensure its accuracy. Be sure to let our advertisers know you found them through IEEE Signal Processing Magazine.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

The authors of [1], which was published in the November 2017 issue of IEEE Signal Processing Magazine, wish to add an acknowledgment to their article. The acknowledgment is as follows: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (number 2016R1A2B2014525) and by a grant from the National Science Foundation (IIS-1116656) awarded to Alan C. Bovik.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Incorporating graphs in the analysis of multivariate signals is becoming a standard way to understand the interdependency of activity recorded at different sites. The new research frontier in this direction includes the important problem of how to assess dynamic changes of signal activity. We address this problem in a novel way by defining the graph-variate signal alongside methods for its analysis.