Title

Why Sparse?

Publication Date

12-2017

Comments

Technical Report: UTEP-CS-17-96

To appear in: Olga Kosheleva, Sergey Shary, Gang Xiang, and Roman Zapatrin (eds.), Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy, etc. Methods and Their Applications, Springer, Cham, Switzerland, 2018.

Abstract

In many situations, a solution to a practical problem is sparse, i.e., corresponds to the case when most of the parameters describing the solution are zeros, and only a few attain non-zero values. This surprising empirical phenomenon helps solve the corresponding problems -- but it remains unclear why this phenomenon happens. In this paper, we provide a possible theoretical explanation for this mysterious phenomenon.

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