Search Results - subject:"Low-rank approximation"

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A general error analysis for randomized low-rank approximation with application to data assimilation

Scotto di Perrotolo, Alexandre; Diouane, Youssef; … - 2024

Persistent link: https://www.econbiz.de/10014575760

Adaptive joint distribution learning

Filipović, Damir; Multerer, Michael; Schneider, Paul - 2024

Persistent link: https://www.econbiz.de/10015110750

Low-rank matrix approximation with weights or missing data is NP-hard

GILLIS, Nicolas; GLINEUR, François - Center for Operations Research and Econometrics (CORE), … - 2010

Weighted low-rank approximation (WLRA), a dimensionality reduction technique for data analysis, has been successfully …

Persistent link: https://www.econbiz.de/10008836126

Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework

Kim, Jingu; He, Yunlong; Park, Haesun - In: Journal of Global Optimization 58 (2014) 2, pp. 285-319

) from a unified view based on the block coordinate descent (BCD) framework. NMF and NTF are low-rank approximation methods …

Persistent link: https://www.econbiz.de/10010896402

Dynamical low-rank approximation: applications and numerical experiments

Nonnenmacher, Achim; Lubich, Christian - In: Mathematics and Computers in Simulation (MATCOM) 79 (2008) 4, pp. 1346-1357

Dynamical low-rank approximation is a differential-equation-based approach to efficiently compute low …

Persistent link: https://www.econbiz.de/10010870388

A study of singular spectrum analysis with global optimization techniques

Chu, Moody; Lin, Matthew; Wang, Liqi - In: Journal of Global Optimization 60 (2014) 3, pp. 551-574

Singular spectrum analysis has recently become an attractive tool in a broad range of applications. Its main mechanism of alternating between rank reduction and Hankel projection to produce an approximation to a particular component of the original time series, however, deserves further...

Persistent link: https://www.econbiz.de/10010938214

Optimization challenges in the structured low rank approximation problem

Gillard, Jonathan; Zhigljavsky, Anatoly - In: Journal of Global Optimization 57 (2013) 3, pp. 733-751

In this paper we illustrate some optimization challenges in the structured low rank approximation (SLRA) problem. SLRA … can be described as the problem of finding a low rank approximation of an observed matrix which has the same structure as …

Persistent link: https://www.econbiz.de/10010896448