On the minimax risk of dictionary learning
WebOn 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, ... http://www.inspirelab.us/wp-content/uploads/2024/07/ShakeriSarwateEtAl.BookChInfoTh21-Preprint.pdf
On the minimax risk of dictionary learning
Did you know?
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. 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 ...
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 ... 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.
WebBibliographic details on On the Minimax Risk of Dictionary Learning. DOI: — access: open type: Informal or Other Publication metadata version: 2024-08-13 WebThis paper provides fundamental limits on the sample complexity of estimating dictionaries for tensor data. ... Minimax Lower Bounds on Dictionary Learning for Tensor Data ...
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.
Web3 de abr. de 2024 · The NEUSS model first derives the asset embeddings for each asset (ETF) based on its financial news and machine learning methods such as UMAP, paragraph models and word embeddings. Then we obtain a collection of the basis assets based on their asset embeddings. After that, for each stock, we select the basis assets to … birchfield fish bar northamptonWebminimax 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 … birchfield gas services greensburg paWebIt is assumed the data are generated by linear combinations of these structured dictionary atoms and observed through white Gaussian noise. 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 … birchfield garageWeb[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 … dallas cowboys t shirt womenWebMinimax lower bounds for Kronecker-structured dictionary learning. Authors: Zahra Shakeri. Dept. of Electrical and Computer Engineering, Rutgers University, Piscataway, New Jersey 08854, United States ... dallas cowboys twitter feedWeb17 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 … birchfield gospel hallWebDictionary 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 … dallas cowboys t-shirts vintage