Results 261 to 270 of about 37,640 (313)

Difference Subspace and Its Generalization for Subspace-Based Methods

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
Subspace-based methods are known to provide a practical solution for image set-based object recognition. Based on the insight that local shape differences between objects offer a sensitive cue for recognition, this paper addresses the problem of extracting a subspace representing the difference components between class subspaces generated from each set
Kazuhiro Fukui, Atsuto Maki
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A framework for subspace identification methods

Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001
Similarities and differences among various subspace identification methods (MOESP, N4SID and CVA) are examined by putting them in a general regression framework. Subspace identification methods consist of three steps: estimating the predictable subspace for multiple future steps, then extracting state variables from this subspace and finally fitting ...
Ruijie Shi, John F. MacGregor
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Subspace methods for robot vision

IEEE Transactions on Robotics and Automation, 1996
In contrast to the traditional approach, visual recognition is formulated as one of matching appearance rather than shape. For any given robot vision task, all possible appearance variations define its visual workspace. A set of images is obtained by coarsely sampling the workspace.
Shree K. Nayar, Sameer A. Nene, Hiroshi Murase   +2 more
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Nonstationary Consistency of Subspace Methods

IEEE Transactions on Automatic Control, 2007
In this paper, we study ldquononstationary consistencyrdquo of subspace methods for eigenstructure identification, i.e., the ability of subspace algorithms to converge to the true eigenstructure despite nonstationarities in the excitation and measurement noises. Note that such nonstationarities may result in having time-varying zeros for the underlying
Albert Benveniste, Laurent Mevel
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Boosting random subspace method

Neural Networks, 2008
In this paper we propose a boosting approach to random subspace method (RSM) to achieve an improved performance and avoid some of the major drawbacks of RSM. RSM is a successful method for classification. However, the random selection of inputs, its source of success, can also be a major problem.
Nicolás García-Pedrajas, Domingo Ortiz-Boyer   +1 more
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Subspace methods for computational relighting

SPIE Proceedings, 2013
We propose a vector space approach for relighting a Lambertian convex object with distant light source, whose crucial task is the decomposition of the reflectance function into albedos (or reflection coefficients) and lightings based on a set of images of the same object and its 3-D model.
Ha Q. Nguyen 0001, Siying Liu, Minh N. Do   +2 more
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The subspace method in Hilbert space

Systems and Computers in Japan, 2001
AbstractThe subspace method has usually been applied to a multidimensional space (i.e., feature space) which uses features as its basis. A subspace method can also be applied to a functional space, since the subspace can be defined by an arbitrary linear space. This paper proposes the mapping of a feature space onto the Hilbert subspace so that pattern
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On Moment Methods in Krylov Subspaces

Doklady Mathematics, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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From the Subspace Methods to the Mutual Subspace Method

2010
The Subspace Method [25, 21] is a classic method of pattern recognition, and has been applied to various tasks. The Mutual Subspace Method [19] is an extension of the Subspace Methods, in which canonical angles (principal angles) between two subspaces are used to define similarity between two patterns (or two sets of patterns). The method is applied to
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A Krylov Subspace Method for Information Retrieval

SIAM Journal on Matrix Analysis and Applications, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Katarina Blom, Axel Ruhe
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