On the minimax risk of dictionary learning
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 … 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 …
On the minimax risk of dictionary learning
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Web1 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. 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 …
Web9 de mar. de 2024 · The lower bound follows from a lower bound on the minimax risk for general coefficient distributions and can be further specialized to sparse-Gaussian coefficients. This bound scales linearly with the sum of the product of the dimensions of the (smaller) coordinate dictionaries for tensor data. Webminimax risk have direct implications on the required sample size of accurate DL schemes. In particular our analysis reveals that, for a sufficiently incoherent underlying …
Web17 de mai. de 2016 · In this regard, the paper provides a general lower bound on the minimax risk and also adapts the proof techniques for equivalent results using sparse and Gaussian coefficient models. The reported results suggest that the sample complexity of dictionary learning for tensor data can be significantly lower than that for unstructured … Web17 de fev. de 2014 · Prior theoretical studies of dictionary learning have either focused on existing algorithms for non-KS dictionaries [5,[16][17][18][19][20][21] or lower bounds on …
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.
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 ... popmoney sign upWebWe 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 … popmoney limit transferWeb: (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 share video files easilyWeb15 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. popmoney servicehttp://www.inspirelab.us/wp-content/uploads/2024/07/ShakeriSarwateEtAl.BookChInfoTh21-Preprint.pdf share video files for editingWebRelevant books, articles, theses on the topic 'Estimation de la norme minimale.' Scholarly sources with full text pdf download. Related research topic ideas. popmoney credit card feesWebThis paper identifies minimax rates of CSDL in terms of reconstruction risk, providing both lower and upper bounds in a variety of settings. Our results make minimal assumptions, … pop monthey