On the minimax risk of 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 … WebThis 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, …
On the minimax risk of dictionary learning
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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 ... 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 …
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 …
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 … 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
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.
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. canon af trackingWebData 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 ... flag of germany svgWebWe 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 … canon alberic\u0027s scrapbook textWeb30 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. canon air chaud indirectWebWe 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 … flag of germany color meaningWebOn the minimax risk of dictionary learning. Alexander Jung, Yonina C. Eldar, Norbert Görtz. Department of Computer Science; ... Abstract. We 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 dictionary. In particular, ... flag of germany pictureWebWe 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 … canon alberic\u0027s scrap-book