On the minimax risk of dictionary learning

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August undefined, 2024

Web15 de jul. de 2016 · The focus of this paper is on second-order tensor data, with the underlying dictionaries constructed by taking the Kronecker product of two smaller … Webthe information theory literature; these include restating the dictionary learning problem as a channel coding problem and connecting the analysis of minimax risk in statistical estimation to Fano’s inequality. In addition to highlighting the effects of different parameters on the sample complexity of dictionary learning,

Sample complexity bounds for dictionary learning of tensor data

WebTranslations in context of "contenute a" in Italian-English from Reverso Context: a quelle contenute Web: (7) A. Minimax risk analysis We are interested in lower bounding the minimax risk for estimating D based on observations Y, which is deﬁned as the worst-case mean squared error (MSE) that can be obtained by the best KS dictionary estimator Db(Y). That is, " = inf Db sup 2X(0;r) E Y n Db(Y) D 2 F iphone leather case with card holder

Minimax Lower Bounds for Kronecker-Structured Dictionary Learning

WebOn the Minimax Risk of Dictionary Learning Alexander Jung, Yonina C. Eldar,Fellow, IEEE, and Norbert Görtz,Senior Member, IEEE Abstract—We consider the problem of … http://www.inspirelab.us/wp-content/uploads/2024/07/ShakeriSarwateEtAl.BookChInfoTh21-Preprint.pdf WebIndex Terms—Compressed sensing, dictionary learning, minimax risk, Fano inequality. I. INTRODUCTION A CCORDING to [1], the worldwide internet trafﬁc in 2016 will exceed the Zettabyte threshold.1 In view of the pervasive massive datasets generated at an ever increasing speed [2], [3], it is mandatory to be able to extract relevant iphonelife reviews

Learning fast dictionaries for sparse representations using low …

On the Minimax Risk of Dictionary Learning - IEEE Journals

WebDictionary learning is the problem of estimating the collection of atomic elements that provide a sparse representation of measured/collected signals or data. This paper finds fundamental limits on the sample complexity of estimating dictionaries for tensor data by proving a lower bound on the minimax risk. WebIn particular, we analyze the minimax risk of the dictionary learning problem which governs the mean squared error (MSE) performance of any learning scheme, regardless of its computational complexity. iphone leather vs silicone caseWebminimax risk for the dictionary identiﬁability problem showed that the necessary number of samples for reliable reconstruction, ... 2 A Dictionary Learning AlgorithmforTensorial Data 2.1 (R,K)-KS dictionary learning model Given … iphone lifeproof case 5c

"Web30 de jan. de 2024 · Minimax Lower Bounds on Dictionary Learning for Tensor Data Abstract: This paper provides fundamental limits on the sample complexity of estimating … " - On the minimax risk of dictionary learning

On the minimax risk of dictionary learning

Jian Ma :: Carnegie Mellon School of Computer Science

WebMinimax lower bounds for Kronecker-structured dictionary learning. Authors: Zahra Shakeri. Dept. of Electrical and Computer Engineering, Rutgers University, Piscataway, New Jersey 08854, United States ... WebWe consider the problem of learning a dictionary matrix from a number of observed signals, which are assumed to be generated via a linear model with a common underlying …

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Web9 de ago. de 2016 · This work first provides a general lower bound on the minimax risk of dictionary learning for such tensor data and then adapts the proof techniques for … WebKS dictionary. The risk decreases with larger Nand K; in particular, larger Kfor ﬁxed mpmeans more structure, which simpliﬁes the estimation problem. The results for …

WebMinmax (sometimes Minimax, MM or saddle point) is a decision rule used in artificial intelligence, decision theory, game theory, statistics, and philosophy for minimizing the possible loss for a worst case (maximum loss) scenario.When dealing with gains, it is referred to as "maximin" – to maximize the minimum gain. Originally formulated for … Web29 de ago. de 2024 · On the Minimax Risk of Dictionary Learning Article Full-text available Jul 2015 IEEE T INFORM THEORY Alexander Jung Yonina Eldar Norbert Goertz We consider the problem of learning a...

WebDownload scientific diagram Two η(x) used for the proof of Theorem 3 when d = 1 from publication: Minimax-Optimal Bounds for Detectors Based on Estimated Prior Probabilities In many signal ... WebWe consider the problem of learning a dictionary matrix from a number of observed signals, which are assumed to be generated via a linear model with a common underlying …

WebWe consider the problem of learning a dictionary matrix from a number of observed signals, which are assumed to be generated via a linear model with a common underlying … iphone lifeproof case warranty claimWebminimax risk have direct implications on the required sample size of accurate DL schemes. In particular our analysis reveals that, for a sufﬁciently incoherent underlying … iphone lidar safetyWebWe consider the problem of learning a dictionary matrix from a number of observed signals, which are assumed to be generated via a linear model with a common... Skip to … iphone lidar 波長WebWe consider the problem of dictionary learning under the assumption that the observed signals can be represented as sparse linear combinations of the columns of a single … iphone life 360 hackWeb22 de mar. de 2024 · A new algorithm for dictionary learning based on tensor factorization using a TUCKER model, in which sparseness constraints are applied to the core tensor, of which the n-mode factors are learned from the input data in an alternate minimization manner using gradient descent. Expand 72 PDF View 1 excerpt, references methods iphone lifetimeWeb1 de abr. de 2024 · This work first provides a general lower bound on the minimax risk of dictionary learning for such tensor data and then adapts the proof techniques for specialized results in the case of sparse and sparse-Gaussian linear combinations. iphone lifeproof skinsWebData Scientist with 2 years of industry experience in requirements gathering, predictive modeling on large data sets, and visualization. Proficient in generating data-driven business insights and ... iphone lidar speed gun