Results 251 to 260 of about 702,701 (291)

Principal Components Analysis and Orthogonal Projection

2022
_ Okamoto(2006) (see the file okamoto_2006.pdf in the Files box below) explains principal components analysis (PCA) from the point of view of orthogonal projection. Choice of the bases in the projected space is arbitrary, that is, rotation of the basis is allowed. Deisenroth et al. (2006) also explains PCA in terms of an orthonormal projection.
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Adaptive distributed orthogonalization processing for principal components analysis

[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992
Adaptive extraction of principal components of a vector stochastic process is a topic currently receiving much attention. The authors propose a learning algorithm implemented on a neural-like network. This algorithm is shown to be superior to previous ones. The convergence of this algorithm can be proved, but only an outline of the proof is presented. >
Hong Chen, Ruey-Wen Liu
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Orthogonal coordinates for systems of many components

Metallurgical Transactions A, 1985
We show how a set of orthogonal coordinates is defined for a system having an arbitrary number of components. These coordinates, in conjunction with the limiting atom fractions of the components, define the equilateral triangle for a three component system and a regular tetrahedron for a four component system.
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A four-component organogel based on orthogonal chemical interactions

Chem. Commun., 2014
A thermoresponsive organogel was obtained by orthogonal assembly of four compounds using dynamic covalent boronate ester and imine bonds, as well as dative boron–nitrogen bonds.
Luisier Nicolas, Schenk Kurt, Severin Kay   +2 more
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Independent Detectors for Expansion and Rotation, and for Orthogonal Components of Deformation

Perception, 2001
It is well known that optic flow—the smooth transformation of the retinal image experienced by a moving observer—contains valuable information about the three-dimensional layout of the environment. From psychophysical and neurophysiological experiments, specialised mechanisms responsive to components of optic flow (sometimes called complex motion ...
T S, Meese, M G, Harris
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Non Orthogonal Component Analysis: Application to Anomaly Detection

2006
Independent Component Analysis (ICA) has shown success in blind source separation. Its applications to remotely sensed images have been investigated recently. In this approach, a Linear Spectral Mixture (LSM) model is used to characterize spectral data.
Gaucel, Jean-Michel, Guillaume, Mireille, Bourennane, Salah   +2 more
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On the Expected Prediction Error of Orthogonal Regression with Variable Components

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2006
In this article, we considered the asymptotic expectations of the prediction error and the fitting error of a regression model, in which the component functions are chosen from a finite set of orthogonal functions. Under the least squares estimation, we showed that the asymptotic bias in estimating the prediction error based on the fitting error ...
Katsuyuki Hagiwara, Hiroshi Ishitani
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Image compression using orthogonalized independent components bases

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718), 2003
In this paper we address the orthogonalization of independent component analysis (ICA) to obtain transform-based image coders. We consider several classes of training images, from which we extract the independent components, followed by orthogonalization, obtaining bases for image coding.
Artur J. Ferreira, Mário A. T. Figueiredo   +1 more
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Approximate partitioning of 2D objects into orthogonally convex components

Computer Vision and Image Understanding, 2013
A fast and efficient algorithm to obtain an orthogonally convex decomposition of a digital object is presented. The algorithm reports a sub-optimal solution and runs in O(nlogn) time for a hole-free object whose boundary consists of n pixels. The decomposition algorithm can, in fact, be applied on any hole-free orthogonal polygon; in our work, it is ...
Mousumi Dutt, Arindam Biswas 0002, Partha Bhowmick   +2 more
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Learning independent components on the orthogonal group of matrices by retractions

Neural Processing Letters, 2007
Neural independent component learning algorithms based on optimization on manifolds have attracted interest in the neural network community. In the past years, we have developed learning algorithms specialized for the orthogonal group of matrices as parameters manifold.
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