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[电气工程与系统科学] 稀疏盲反卷积中的结构化局部最优 [推广有奖]

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可人4 在职认证  发表于 2022-3-15 09:20:00 来自手机 |只看作者 |坛友微信交流群|倒序 |AI写论文

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摘要翻译:
盲反卷积是从两个未知信号的卷积中恢复它们的一个普遍存在的问题。不幸的是,这是一个不适定的问题。本文研究了一个未知信号是短信号,另一个未知信号是稀疏随机支持的{em短稀疏}盲反卷积问题。这种变体捕捉了几个重要应用中未知信号的结构。假设短信号具有单位$\ell^2$范数,将盲反卷积问题转化为球面上的非凸优化问题。证明了:(一)在球面的某一区域内,每个局部最优值都接近于基真核的某个移位截断值;(二)对于一般的长度为k$的短信号,当激活信号的稀疏性为theta\lesssim k^{-2/3}$和测量值的个数为m\gtrsim poly(k)$时,简单的初始化方法和逃避严格鞍点的下降算法可以恢复基真核的一个近移位截断值。
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英文标题:
《Structured Local Optima in Sparse Blind Deconvolution》
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作者:
Yuqian Zhang and Han-Wen Kuo and John Wright
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最新提交年份:
2019
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分类信息:

一级分类:Electrical Engineering and Systems Science        电气工程与系统科学
二级分类:Signal Processing        信号处理
分类描述:Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用,包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、(生物)医学、图像、视频和多模态自然和人为信号,包括通信信号和数据。感兴趣的主题包括:统计信号处理、谱估计和系统辨识;滤波器设计;自适应滤波/随机学习;(压缩)采样、传感和变换域方法,包括快速算法;用于机器学习的信号处理和用于信号处理应用的机器学习;网络与图形信号处理;信号处理中的凸和非凸优化方法;雷达、声纳和传感器阵列波束形成和测向;通信信号处理;低功耗、多核、片上系统信号处理;信息物理系统的传感、通信、分析和优化,如电网和物联网。
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一级分类:Computer Science        计算机科学
二级分类:Information Theory        信息论
分类描述:Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
涵盖信息论和编码的理论和实验方面。包括ACM学科类E.4中的材料,并与H.1.1有交集。
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一级分类:Mathematics        数学
二级分类:Information Theory        信息论
分类描述:math.IT is an alias for cs.IT. Covers theoretical and experimental aspects of information theory and coding.
它是cs.it的别名。涵盖信息论和编码的理论和实验方面。
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一级分类:Mathematics        数学
二级分类:Optimization and Control        优化与控制
分类描述:Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学,线性规划,控制论,系统论,最优控制,博弈论
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一级分类:Statistics        统计学
二级分类:Machine Learning        机器学习
分类描述:Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文(监督,无监督,半监督学习,图形模型,强化学习,强盗,高维推理等)与统计或理论基础
--

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英文摘要:
  Blind deconvolution is a ubiquitous problem of recovering two unknown signals from their convolution. Unfortunately, this is an ill-posed problem in general. This paper focuses on the {\em short and sparse} blind deconvolution problem, where the one unknown signal is short and the other one is sparsely and randomly supported. This variant captures the structure of the unknown signals in several important applications. We assume the short signal to have unit $\ell^2$ norm and cast the blind deconvolution problem as a nonconvex optimization problem over the sphere. We demonstrate that (i) in a certain region of the sphere, every local optimum is close to some shift truncation of the ground truth, and (ii) for a generic short signal of length $k$, when the sparsity of activation signal $\theta\lesssim k^{-2/3}$ and number of measurements $m\gtrsim poly(k)$, a simple initialization method together with a descent algorithm which escapes strict saddle points recovers a near shift truncation of the ground truth kernel.
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PDF链接:
https://arxiv.org/pdf/1806.00338
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关键词:结构化 Applications Optimization Experimental Measurements 移位 鞍点 blind 盲反 信号

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