On the minimax risk of dictionary learning

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Web22 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 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 ...

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Web12 de jan. de 2016 · On the Minimax Risk of Dictionary Learning Abstract: We consider the problem of learning a dictionary matrix from a number of observed signals, which … 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 … christian relikia

Sample complexity bounds for dictionary learning of tensor data

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, … WebThis paper provides fundamental limits on the sample complexity of estimating dictionaries for tensor data. ... Minimax Lower Bounds on Dictionary Learning for Tensor Data ... christian raulin karlsruhe

Minimax lower bounds for Kronecker-structured dictionary learning ...

Minimax Lower Bounds for Dictionary Learning from Tensor Data

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 ... Web[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 … christian rijantoWebWe 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 … christian rinke deloitte

"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 … " - On the minimax risk of dictionary learning

On the minimax risk of dictionary learning

Two η(x) used for the proof of Theorem 3 when d = 1

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 … 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.

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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. Skip to Main Content. IEEE.org; IEEE Xplore Digital Library; IEEE-SA; IEEE ... On the Minimax Risk of Dictionary Learning Webminimax 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 …

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 ... 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 …

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 … 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 …

WebConjugation Documents Dictionary Collaborative Dictionary Grammar Expressio Reverso Corporate. ... Minimax hat die erste Brandschutzlösung für Schiffsbalkone entwickelt, ... conjugation, learning games. Results: 127. Exact: 127. Elapsed time: 163 ms. Documents Corporate solutions Conjugation Synonyms Grammar Check Help & about. Word index: …

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 … christian rokitta getquinWebDictionary 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 … christian reisen tirolWeb20 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 … christian rojas usmWebthe 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, christian robinson pajamasWebminimax risk have direct implications on the required sample size of accurate DL schemes. In particular our analysis reveals that, for a sufﬁciently incoherent underlying … christian rosankaWeb1 de mar. de 2024 · This paper provides fundamental limits on the sample complexity of estimating dictionaries for tensor data. The specific focus of this work is on $K$th-order tensor data and the case where the... christian ronjatWebBibliographic details on On the Minimax Risk of Dictionary Learning. DOI: — access: open type: Informal or Other Publication metadata version: 2024-08-13 christian rudenäs