Results 151 to 160 of about 328,340 (188)

Lambertian reflectance and linear subspaces

Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2002
We prove that the set of all Lambertian reflectance functions (the mapping from surface normals to intensities) obtained with arbitrary distant light sources lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated ...
R. Basri, D.W. Jacobs
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Sparse Subspace Clustering with Linear Subspace-Neighborhood-Preserving Data Embedding

2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2020
Data dimensionality reduction via linear embedding is a typical approach to economizing the computational cost of machine learning systems. In the context of sparse subspace clustering (SSC), this paper proposes a two-step neighbor identification scheme using linear neighborhoodpreserving embedding. In the first step, a quadratically- constrained l 1 -
Jwo-Yuh Wu, Liang-Chi Huang, Wen-Hsuan Li, Hau-Hsiang Chan, Chun-Hung Liu, Rung-Hung Gau   +5 more
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A Subspace Error Estimate for Linear Systems

SIAM Journal on Matrix Analysis and Applications, 2003
Summary: This paper proposes a new method for estimating the error in the solution of linear systems. A condition number is defined for a linear function of the solution components. This definition of the condition number is quite versatile. It reduces to the component condition number proposed by \textit{S. Chandrasekaran} and \textit{I. C. F. Ipsen} [
Cao, Yang, Petzold, Linda
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Testing Hypotheses concerning Unions of Linear Subspaces

Journal of the American Statistical Association, 1984
Abstract The likelihood ratio test (LRT) for hypotheses concerning unions of linear subspaces is derived for the normal theory linear model. A more powerful test, an intersection-union test, is proposed for the case in which the subspaces are not all of the same dimension.
Roger L. Berger, Dennis F. Sinclair
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Combining Linear Dimension Reduction Subspaces

2016
Dimensionality is a major concern in the analysis of large data sets. There are various well-known dimension reduction methods with different strengths and weaknesses. In practical situations it is difficult to decide which method to use as different methods emphasize different structures in the data.
Liski, E., Nordhausen, Klaus, Oja, H., Ruiz-Gazen, Anne   +3 more
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Linear Subspaces and Affine Manifolds

1976
The first three chapters of this book discuss the concept of linear subspaces and some of its important subsets — namely, affine manifolds, convex cones and sets. The notion of convexity plays a dominant role in nonlinear programming and is explored in depth in these chapters. Chapter 4 deals with convex and convex-like functions. As we will see later,
M. S. Bazaraa, C. M. Shetty
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Quasi-Adaptive NIZK for Linear Subspaces Revisited

2015
Non-interactive zero-knowledge (NIZK) proofs for algebraic relations in a group, such as the Groth-Sahai proofs, are an extremely powerful tool in pairing-based cryptography. A series of recent works focused on obtaining very efficient NIZK proofs for linear spaces in a weaker quasi-adaptive model.
Eike Kiltz, Hoeteck Wee
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ESTIMATING LINEAR DYNAMICAL SYSTEMS USING SUBSPACE METHODS

Econometric Theory, 2005
This paper provides a survey on a class of so-called subspace methods whose main proponent is CCA proposed by Larimore (1983, Proceedings of the 1983 American Control Conference 2). Because they are based on regressions these methods for the estimation of ARMAX systems are attractive as a result of their conceptual simplicity and their numerical ...
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A Characterization of Linear Subspaces

2004
In this chapter we prove the following characterization of linear subspaces: Theorem 6.1 (Van de Ven [144]). Let Y be a smooth closed irreducible subvariety of ℙ n of dimension d ≥ 1 over the field \(\mathbb{C}\) of complex numbers. Then the normal sequence $$0 \to T_Y \to T_{P^n } |Y \to N_{Y|\mathbb{P}^n } \to 0$$ splits if and only if Y is a ...
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Online Tracking of Linear Subspaces

2006
We address the problem of online de-noising a stream of input points. We assume that the clean data is embedded in a linear subspace. We present two online algorithms for tracking subspaces and, as a consequence, de-noising. We also describe two regularization schemas which improve the resistance to noise.
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