Results 1 to 10 of about 26,996 (193)
Subspace Recycling--Based Regularization Methods [PDF]
SIAM Journal on Matrix Analysis and Applications, 2021 Subspace recycling techniques have been used quite successfully for the acceleration of iterative methods for solving large-scale linear systems. These methods often work by augmenting a solution subspace generated iteratively by a known algorithm with a fixed subspace of vectors which are ``useful'' for solving the problem.
Ronny Ramlau +2 more
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Subspace Methods for Nonlinear Optimization
CSIAM Transactions on Applied Mathematics, 2021 Summary: Subspace techniques such as Krylov subspace methods have been well known and extensively used in numerical linear algebra. They are also ubiquitous and becoming indispensable tools in nonlinear optimization due to their ability to handle large scale problems. There are generally two types of principals: i) the decision variable is updated in a
Xin Liu +2 more
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Subspace Methods for Multimicrophone Speech Dereverberation [PDF]
EURASIP Journal on Advances in Signal Processing, 2003 Summary: A novel approach for multimicrophone speech dereverberation is presented. The method is based on the construction of the null subspace of the data matrix in the presence of colored noise, using the generalized singular-value decomposition technique, or the generalized eigenvalue decomposition of the respective correlation matrices. The special
Moonen Marc, Gannot Sharon
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The detection and correction of silent errors in pipelined Krylov subspace methods. [PDF]
Numer AlgorithmsCarson EC, Hercík J.
europepmc +3 more sources
On subspace system identification methods [PDF]
Modeling, Identification and Control, 2022 An open and closed loop subspace system identification algorithm DSRe is compared to competitive open loop algorithms, DSR, and N4SID. Additionally, DSRe is compared vs the optimal Prediction Error Method (PEM).
Christer Dalen, David Di Ruscio
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IEEE Access, 2021 This manuscript deals with the system identification of lightly (under) damped processes to find a suitable model structure. To identify such processes, the Kautz model, which is a two-parameter representation of orthogonal basis functions (OBF) in state
R. Rajaprasanna +2 more
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IEEE Access, 2022 This paper tackles the issue of real-time parametric estimation of a wide class of probability density functions from limited datasets. This type of estimation addresses recent applications that require joint sensing and actuation.
Ahmad A. Masoud
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APPLICATION OF PARTIAL LEAST SQUARES REGRESSION FOR AUDIO-VISUAL SPEECH PROCESSING AND MODELING [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2015 Subject of Research. The paper deals with the problem of lip region image reconstruction from speech signal by means of Partial Least Squares regression. Such problems arise in connection with development of audio-visual speech processing methods.
A. L. Oleinik
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Combined Deterministic and Stochastic System Identification and Realization: DSR - A Subspace Approach Based on Observations [PDF]
Modeling, Identification and Control, 1996 A numerically stable and general algorithm for identification and realization of a complete dynamic linear state space model, including the system order, for combined deterministic and stochastic systems from time series is presented.
David Di Ruscio
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Slow feature subspace: A video representation based on slow feature analysis for action recognition
Machine Learning with Applications, 2023 This paper proposes a new video representation for subspace-based action recognition. Traditional subspace-based methods represent a video as a subspace by applying principal component analysis (PCA) to its frames. However, this subspace might lead to an
Suzana Rita Alves Beleza +3 more
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