Results 231 to 240 of about 136,791 (265)
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Orthogonal Components in MANOVA with Application to an Anesthesiology Experiment
Biometrical Journal, 1988AbstractWe describe orthogonal components in multivariate analysis of variance, and illustrate their value when assessing restricted alternatives.
James A Koziol
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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, 1992Adaptive 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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A four-component organogel based on orthogonal chemical interactions
Chem. Commun., 2014A 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 +2 more
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Approximate partitioning of 2D objects into orthogonally convex components
Computer Vision and Image Understanding, 2013A 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 +2 more
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Integrating Orthogonal Middleware Functionality in Components Using Interceptors
2003Current component platforms usually consider only a limited set of non-functional properties. Integration of these aspects is moreover handled in a rather static way. This article elaborates on possible uses of existing meta-programming facilities, notably interceptors, for custom integration of orthogonal middleware facilities.
Christoph Pohl, Steffen Göbel 0001
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On the Equivocation Region of Relay Channels with Orthogonal Components
2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers, 2007We consider the secrecy rate of a relay network where an eavesdropper is co-located with the relay node. This exemplifies a scenario where the relay node is not malicious by nature, but is located in an "untrusted region", and hence is potentially compromised. Given that the aim now is to keep the relay node completely oblivious to the information sent
Xiang He, Aylin Yener
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Learning independent components on the orthogonal group of matrices by retractions
Neural Processing Letters, 2007Neural 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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Orthogonal learning network for constrained principal component problem
1990 IJCNN International Joint Conference on Neural Networks, 1990The regular principal components (PC) analysis of stochastic processes is extended to the constrained principal components (CPC) problem. As in the PC analysis, the CPC analysis involves extracting representative components which contain the most information about the original processes.
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Principal Components and Orthogonal Regression Based on Robust Scales
Technometrics, 2005Both principal components analysis (PCA) and orthogonal regression deal with finding a p-dimensional linear manifold minimizing a scale of the orthogonal distances of the m-dimensional data points to the manifold. The main conceptual difference is that in PCA p is estimated from the data, to attain a small proportion of unexplained variability, whereas
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Discrimination by means of components that are orthogonal in the data space
Journal of Chemometrics, 1997Krzanowski (J. Chemometrics, 9, 509 (1995)) proposed a method for obtaining so-called orthogonal canonical variates (henceforth called components) for discrimination purposes. In contrast with ordinary discriminant analysis, this method employs components that are orthogonal in the original data space.
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