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題目：Nonconvex Approaches in Data Science

報告人：婁易非（University of Texas Dallas）

時間：2020年12月11日上午9:00

摘要：Although ``big data'' is ubiquitous in data science, one often faces challenges of ``small data,'' as the amount of data that can be taken or transmitted is limited by technical or economic constraints. To retrieve useful information from the insufficient amount of data, additional assumptions on the signal of interest are required, e.g. sparsity (having only a few non-zero elements). Conventional methods  favor incoherent systems, in which any two measurements are as little correlated as possible. In reality, however, many problems are coherent.  I will present two nonconvex approaches: one is the difference of the $L_1$ and $L_2$ norms and the other is the ratio of the two. The difference model works particularly well in the coherent regime, while the ratio is a scale-invariant metric that works better when underlying signals have large fluctuations  in non-zero values. Various numerical experiments have demonstrated advantages of the proposed methods over the state-of-the-art. Applications, ranging from limited-angle CT reconstruction to low-rank approximation, will be discussed.

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邀請人：陶敏老師