Results 61 to 70 of about 62,622 (323)
Ultramicroscopy, 2020 Tensor singular value decomposition (SVD) is a method to find a low-dimensional representation of data with meaningful structure in three or more dimensions.
Chenyu Zhang +3 more
semanticscholar +1 more source
The Anatomical Record, EarlyView.Abstract Analysis of the variation in the bony structures of the inner and middle ear provides critical insights into functional morphology, as well as adaptive morphology across primates. In this study, we investigated whether ear morphology patterns are related to the ecological characteristics of species and their habitats to test two acoustic ...
Myriam Marsot +4 more
wiley +1 more source
The Singular Value Expansion for Arbitrary Bounded Linear Operators
Mathematics, 2020 The singular value decomposition (SVD) is a basic tool for analyzing matrices. Regarding a general matrix as defining a linear operator and choosing appropriate orthonormal bases for the domain and co-domain allows the operator to be represented as ...
Daniel K. Crane, Mark S. Gockenbach
doaj +1 more source
Journal of the Association for Information Science and Technology, EarlyView.Abstract Context‐centric proactive information delivery (PID) is a relatively underexplored domain within recommender systems (RS) aimed at enhancing Knowledge Workers' productivity by proactively providing relevant information during digital tasks.
Mahta Bakhshizadeh +4 more
wiley +1 more source
An Aggregative High-Order Singular Value Decomposition Method in Edge Computing
IEEE Access, 2020 In edge computing, for dimensionality reduction and core data extraction, both edge computing node (ECN) and cloud server may implement a high-order singular value decomposition (HOSVD) algorithm before data are passed to local computing models. However,
Junhua Chen +3 more
doaj +1 more source
Empirical Evaluation of Four Tensor Decomposition Algorithms
, 2007 Higher-order tensor decompositions are analogous to the familiar Singular Value Decomposition (SVD), but they transcend the limitations of matrices (second-order tensors).
Turney, Peter, Turney, Peter D.
core +1 more source
Risk‐aware safe reinforcement learning for control of stochastic linear systems
Asian Journal of Control, EarlyView.Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source
A blind watermarkingalgorithm based on DWT and SVD
Journal of Measurement Science and Instrumentation, 2014 This paper presents a new digital image blind watermarking algorithm based on combination of discrete wavelet transform（DWT）and singular value decomposition（SVD）.First of all,we make wavelet decomposition for the original image and divide the acquired ...
XUAN Chun-qing +3 more
doaj
An Out of Memory tSVD for Big-Data Factorization
IEEE Access, 2020 Singular value decomposition (SVD) is a matrix factorization method widely used for dimension reduction, data analytics, information retrieval, and unsupervised learning.
Hector Carrillo-Cabada +4 more
doaj +1 more source
, 2010 In the Persistent Scatterer Interferometry relative height and deformation updates are estimated between nearby persistent scatterers (PSs). In practice, a reference network is established and the absolute values are estimated by integration.
Parizzi, Alessandro +2 more
core