Results 81 to 90 of about 478,588 (342)
Principal Tensor Embedding for Unsupervised Tensor Learning
IEEE Access, 2020 Tensors and multiway analysis aim to explore the relationships between the variables used to represent the data and find a summarization of the data with models of reduced dimensionality. However, although in this context a great attention was devoted to
Claudio Turchetti +2 more
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International Journal of Adaptive Control and Signal Processing, EarlyView.Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez +2 more
wiley +1 more source
Nonlinear Process Monitoring Based on Global Preserving Unsupervised Kernel Extreme Learning Machine
IEEE Access, 2019 Recently, the unsupervised extreme learning machine (UELM) technique as a nonlinear data mining approach has been employed to diagnose nonlinear process faults.
Hanyuan Zhang +5 more
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Ecology and Evolution, 2023 How to effectively obtain species‐related low‐dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs).
Hao‐Tian Zhang +2 more
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Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence
Algorithms, 2023 Most of the dimensionality reduction algorithms assume that data are independent and identically distributed (i.i.d.). In real-world applications, however, sometimes there exist relationships between data.
Li-Na Wang +3 more
doaj +1 more source
SensorsWith the rapid advancement of remote-sensing technology, the spectral information obtained from hyperspectral remote-sensing imagery has become increasingly rich, facilitating detailed spectral analysis of Earth’s surface objects.
Wenhui Song +5 more
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Differential reductions of the Kadomtsev-Petviashvili equation and associated higher dimensional nonlinear PDEs [PDF]
, 2009 We represent an algorithm allowing one to construct new classes of partially integrable multidimensional nonlinear partial differential equations (PDEs) starting with the special type of solutions to the (1+1)-dimensional hierarchy of nonlinear PDEs linearizable by the matrix Hopf-Cole substitution (the B\"urgers hierarchy).
arxiv +1 more source
Density Matrix Renormalization for Model Reduction in Nonlinear Dynamics [PDF]
, 2007 We present a novel approach for model reduction of nonlinear dynamical systems based on proper orthogonal decomposition (POD). Our method, derived from Density Matrix Renormalization Group (DMRG), provides a significant reduction in computational effort for the calculation of the reduced system, compared to a POD.
arxiv +1 more source
Large Nonlinear $W_{\infty}$ Algebras from Nonlinear Integrable Deformations of Self Dual Gravity [PDF]
Phys. Lett. B353 (1995) 201, 1994 A proposal for constructing a universal nonlinear ${\hat W}_{\infty}$ algebra is made as the symmetry algebra of a rotational Killing-symmetry reduction of the nonlinear perturbations of Moyal-Integrable deformations of $D=4$ Self Dual Gravity (IDSDG).
arxiv +1 more source
Maximum Discriminant Difference Criterion for Dimensionality Reduction of Tensor Data
IEEE Access, 2020 Discriminant analysis is an important tool in machine learning. One of the motivations of this paper is to judge whether a dataset is suitable for discriminant analysis.
Xinya Peng, Zhengming Ma, Haowei Xu
doaj +1 more source