PHASE TRANSITIONS IN THE DYNAMIC MODE DECOMPOSITION ALGORITHM

被引：0

作者：

Prasadan, Arvind ^{[1
]}

Lodhia, Asad ^{[2
]}

Nadakuditi, Raj Rao ^{[1
]}

机构：

[1] Univ Michigan, Dept EECS, Ann Arbor, MI 48109 USA

[2] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA

来源：

2019 IEEE 8TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP 2019) | 2019年

关键词：

Dynamic Mode Decomposition; Singular Value Decomposition; Random Matrix Theory; Source Separation; Time Series; SPECTRAL-ANALYSIS; APPROXIMATION; MATRIX;

D O I：

10.1109/camsap45676.2019.9022604

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We analyze the Dynamic Mode Decomposition (DMD) algorithm in the noisy data setting. Previous work has shown that DMD is a source separation algorithm in disguise, i.e., that it is capable of unmixing linearly mixed time series. In this work, we analyze the performance of DMD when the mixed time series are corrupted by noise. We demonstrate that a pre-processing step of the truncated SVD before applying DMD yields significant benefits, and quantify the performance of the truncated-SVD-plus-DMD (tSVD-DMD) algorithm using tools from random matrix theory. We validate our findings with numerical simulations.

引用

页码：396 / 400

页数：5

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