On the minimax risk of dictionary learning
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 ... 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 …
On the minimax risk of dictionary learning
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WebCORE is not-for-profit service delivered by the Open University and Jisc. 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
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 … 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
WebRelevant books, articles, theses on the topic 'Estimation de la norme minimale.' Scholarly sources with full text pdf download. Related research topic ideas. 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 specialized results in the case of sparse and sparse-Gaussian linear combinations.
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 … pool fencing hobartWebDownload 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 ... shard to london victoriaWeb15 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 … shard to phpWebminimax risk have direct implications on the required sample size of accurate DL schemes. In particular our analysis reveals that, for a sufficiently incoherent underlying … shard to london eyeWebminimax 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 … shard topWeb9 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. pool fencing ingleburnshard to waterloo