On the minimax risk of dictionary learning

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August undefined, 2024

WebSparse decomposition has been widely used in gear local fault diagnosis due to its outstanding performance in feature extraction. The extraction results depend heavily on the similarity between dictionary atoms and fault feature signal. However, the transient impact signal aroused by gear local defect is usually submerged in meshing harmonics and … Web20 de jul. de 2015 · On the Minimax Risk of Dictionary Learning arXiv Authors: Alexander Jung Aalto University Yonina Eldar Weizmann Institute of Science Norbert Görtz Abstract …

IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 62, NO. 3, …

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, 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 … earth eats haddonfield

[1507.05498] On the Minimax Risk of Dictionary Learning

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 … 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. This lower bound depends on the … Web8 de fev. de 2024 · Jung, A., Eldar, Y. C., & Görtz, N. (2016). On the Minimax Risk of Dictionary Learning. IEEE Transactions on Information Theory, 62, 62 earth eats co

Minimax lower bounds for Kronecker-structured dictionary …

On the Minimax Risk of Dictionary Learning - Semantic Scholar

WebMinimax reconstruction risk of convolutional sparse dictionary learning. AISTATS, 2024. Yang Y, Gu Q, Zhang Y, Sasaki T, Crivello J, O'Neill R, Gilbert DM, and Ma J. Continuous-trait probabilistic model for comparing multi-species functional genomic data. Cell Systems, 7(2):208-218.e11 ... WebDownload scientific diagram Examples of R( q) and corresponding η(x) leading to different convergence rates from publication: Minimax-Optimal Bounds for Detectors Based on Estimated Prior ... ct form ct-1120 ext instructionsWebminimax risk have direct implications on the required sample size of accurate DL schemes. In particular our analysis reveals that, for a sufﬁciently incoherent underlying … eartheats 赤羽

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On the minimax risk of dictionary learning

Jian Ma :: Carnegie Mellon School of Computer Science

WebData 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 ... WebThis paper identifies minimax rates of CSDL in terms of reconstruction risk, providing both lower and upper bounds in a variety of settings, and makes minimal assumptions, …

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Web17 de mai. de 2016 · Dictionary learning is the problem of estimating the collection of atomic elements that provide a sparse representation of measured/collected signals or … Web30 de jan. de 2024 · minimax risk of the KS dictionary learning problem for the. case of general coefﬁcient distributions. Theorem 1. Consider a KS dictionary learning problem with.

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 … 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.

Web17 de fev. de 2014 · By following an established information-theoretic method based on Fanos inequality, we derive a lower bound on the minimax risk for a given dictionary learning problem. This lower bound yields a characterization of the sample-complexity, i.e., a lower bound on the required number of observations such that consistent dictionary … Web15 de jul. de 2016 · Minimax lower bounds for Kronecker-structured dictionary learning Abstract: Dictionary learning is the problem of estimating the collection of atomic elements that provide a sparse representation of measured/collected signals or data.

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 comm On the …

ct form ct-1065Web15 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 … earth eats wfiuWeb[28] derived the risk bound for minimax learning by exploiting the dual representation of worst-case risk. However, their minimax risk bound would go to inﬁnity and thus … ct form ct-w4phttp://spars2024.lx.it.pt/index_files/papers/SPARS2024_Paper_10.pdf earth eats 赤羽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 … ct form e159WebIndex 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 ct form dps-164-cWebRelevant books, articles, theses on the topic 'Estimation de la norme minimale.' Scholarly sources with full text pdf download. Related research topic ideas. ct form ed-301