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Subspaces that Minimize the Condition Number of a Matrix
Subspaces Minimize Condition Number Matrix
2015/7/9
We define the condition number of a nonsingular matrix on a subspace, and consider the problem of finding a subspace of given dimension that minimizes the condition number of a given matrix. We give a...
Signal Recovery in Unions of Subspaces with Applications to Compressive Imaging
Union of Subspaces Group Sparsity Convex Optimization Structured Sparsity Compressed Sensing
2012/11/22
In applications ranging from communications to genetics, signals can be modeled as lying in a union of subspaces. Under this model, signal coefficients that lie in certain subspaces are active or inac...
Source Separation and Clustering of Phase-Locked Subspaces: Derivations and Proofs
Index Terms—phase-locking synchrony source separation
2011/7/5
Due to space limitations, our submission "Source Separation and Clustering of Phase-Locked Subspaces", accepted for publication on the IEEE Transactions on Neural Networks in 2011, presented some resu...
Density Estimation and Classification via Bayesian Nonparametric Learning of Affine Subspaces
Dimension reduction Classi er Variable selection Nonparametric Bayes
2011/6/20
It is now practically the norm for data to be very high dimensional in areas such as genetics, machine
vision, image analysis and many others. When analyzing such data, parametric models are often to...
Probabilistic Recovery of Multiple Subspaces in Point Clouds by Geometric lp Minimization
Detection and clustering of subspaces in point clouds hybrid linear modeling lp minimizationas relaxation for l0 minimization
2010/3/10
We assume data independently sampled froma mixture distribution on the unit ball of RD withK+1
components: the first component is a uniform distribution on that ball representing outliers and the oth...
Perturbation expansions of signal subspaces for long signals
Perturbation expansions signal subspaces long signals
2010/3/9
Singular Spectrum Analysis and many other subspace-based methods
of signal processing are implicitly relying on the assumption of close prox-
imity of unperturbed and perturbed signal subspaces extr...
A note on sensitivity of principal component subspaces and the efficient detection of influential observations in high dimensions
distance between subspaces influential observations perturbation principal component analysis
2009/9/16
In this paper we introduce an influence measure based on second order expansion of the RV and GCD measures for the comparison between unperturbed and perturbed eigenvectors of a symmetric matrix estim...