Publications

Universal Features for High-Dimensional Learning and Inference

Shao-Lun Huang, Anuran Makur, Gregory W. Wornell and Lizhong Zheng
Foundations and Trends® in Communications and Information Theory, Vol. 21, No. 1-2, pp.1-299. Now Publishers: Boston - Delft, 2024

e-book

In many contemporary and emerging applications of machine learning and statistical inference, the phenomena of interest are characterized by variables defined over large alphabets. This increasing size of both the data and the number of inferences, and the limited available training data means there is a need to understand which inference tasks can be most effectively carried out, and, in turn, what features of the data are most relevant to them.

In this monograph, the authors develop the idea of extracting “universally good” features, and establish that diverse notions of such universality lead to precisely the same features. The information-theoretic approach used results in a local information geometric analysis that facilitates their computation in a host of applications.

The authors provide a comprehensive treatment that guides the reader through the basic principles to the advanced techniques including many new results. They emphasize a development from first-principles together with common, unifying terminology and notation, and pointers to the rich embodying literature, both historical and contemporary.

Written for students and researchers, this monograph is a complete treatise on the information theoretic treatment of a recognized and current problem in machine learning and statistical inference.

Wireless Communications: Signal Processing Perspectives

H. V. Poor and G. W. Wornell, eds.
Prentice-Hall: Upper Saddle River, NJ, 1998

Signal processing algorithms and architectures have an increasingly important role to play in meeting the central challenges faced in the design of advanced wireless communication systems. In Wireless Communications: Signal Processing Perspectives, leaders in the field describe state-of-the-art research in applying signal processing methodologies in the context of tomorrow’s most important wireless applications, ranging from next-generation cellular telephony and personal communication services, to nomadic computing and wireless multimedia.

Wireless Communications: Signal Processing Perspectives is a valuable reference both for signal processing specialists seeking to apply their expertise in the rapidly growing wireless communications field, and for communications specialists eager to exploit signal processing techniques and implementations in developing efficient wireless systems of the future.

Wireless Communications: Signal Processing Perspectives includes both physical and network layer topics, and also contains a thought-provoking essay by Andrew J. Viterbi on the laws of nature and society that ultimately govern wireless networks.

Signal Processing with Fractals: A Wavelet-Based Approach

G. W. Wornell
Prentice-Hall: Upper Saddle River, NJ, 1996

Fractal geometry and recent developments in wavelet theory are having an important impact on the field of signal processing. Efficient representations for fractal signals based on wavelets are opening up new applications for signal processing, and providing better solutions to problems in existing applications. Signal Processing with Fractals provides a valuable introduction to this new and exciting area, and develops a powerful conceptual foundation for understanding the topic. Practical techniques for synthesizing, analyzing, and processing fractal signal for a wide range of applications are developed in detail, and novel applications in communications are explored. Written by a signal processor for signal processors, Signal Processing with Fractals is a self-contained, well-illustrated treatise, and includes a highly accessible concept-oriented primer of the relevant wavelet theory.