Results 41 to 50 of about 478,588 (342)
eLife, 2021 Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only
Eric Kenji Lee +6 more
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NON-LINEAR AUTOENCODER BASED ALGORITHM FOR DIMENSIONALITY REDUCTION OF AIRBORNE HYPERSPECTRAL DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Hyperspectral remote sensing is an advanced remote sensing technology that enhances the ability of accurate classification due to presence of narrow contiguous bands.
S. Priya, R. Ghosh, B. K. Bhattacharya
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Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering [PDF]
, 2014 Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic
Barrick, TR +3 more
core +1 more source
Fast Tube-Based Robust Compensation Control for Fixed-Wing UAVs
Drones, 2023 When considering the robust control of fixed-wing Unmanned Aerial Vehicles (UAVs), a conflict often arises between addressing nonlinearity and meeting fast-solving requirements.
Lixin Wang +5 more
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Semisupervised Kernel Marginal Fisher Analysis for Face Recognition
The Scientific World Journal, 2013 Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image. To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA ...
Ziqiang Wang +3 more
doaj +1 more source
Remote Sensing, 2020 Accurate and timely information on the spatial distribution of crops is of great significance to precision agriculture and food security. Many cropland mapping methods using satellite image time series are based on expert knowledge to extract ...
Yongguang Zhai +4 more
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Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series [PDF]
, 2007 The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process.
Li, Rui, Sclaroff, Stan, Tian, Tai-Peng
core +3 more sources
Engineering Science and Technology, an International Journal, 2014 Bearing fault, Impeller fault, seal fault and cavitation are the main causes of breakdown in a mono block centrifugal pump and hence, the detection and diagnosis of these mechanical faults in a mono block centrifugal pump is very crucial for its reliable
N.R. Sakthivel +4 more
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Learning a kernel matrix for nonlinear dimensionality reduction [PDF]
, 2004 We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into a nonlinear feature space, we show how to discover a mapping that unfolds ...
Saul, Lawrence K +2 more
core +7 more sources
Finite-dimensional reduction of systems of nonlinear diffusion equations [PDF]
arXiv, 2022 We present a class of one-dimensional systems of nonlinear parabolic equations for which long-time phase dynamics can be described by an ODE with a Lipschitz vector field in R^n. In the considered case of the Dirichlet boundary value problem sufficient conditions for a finite-dimensional reduction turn out to be much wider than the known conditions of ...
arxiv