Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Nadakuditi, Raj Rao"'
Let $A$ be a rectangular matrix of size $m\times n$ and $A_1$ be the random matrix where each entry of $A$ is multiplied by an independent $\{0,1\}$-Bernoulli random variable with parameter $1/2$. This paper is about when, how and why the non-Hermiti
Externí odkaz:
http://arxiv.org/abs/2005.06062
Singular value decomposition (SVD) based principal component analysis (PCA) breaks down in the high-dimensional and limited sample size regime below a certain critical eigen-SNR that depends on the dimensionality of the system and the number of sampl
Externí odkaz:
http://arxiv.org/abs/1905.09369
Autor:
Wu, Hao, Nadakuditi, Raj Rao
We describe a method for unmixing mixtures of freely independent random variables in a manner analogous to the independent component analysis (ICA) based method for unmixing independent random variables from their additive mixtures. Random matrices p
Externí odkaz:
http://arxiv.org/abs/1905.01713
Autor:
Prasadan, Arvind, Nadakuditi, Raj Rao
The Dynamic Mode Decomposition (DMD) extracted dynamic modes are the non-orthogonal eigenvectors of the matrix that best approximates the one-step temporal evolution of the multivariate samples. In the context of dynamical system analysis, the extrac
Externí odkaz:
http://arxiv.org/abs/1903.01310
Publikováno v:
Phys. Rev. E 99, 042309 (2019)
We derive a message passing method for computing the spectra of locally tree-like networks and an approximation to it that allows us to compute closed-form expressions or fast numerical approximates for the spectral density of random graphs with arbi
Externí odkaz:
http://arxiv.org/abs/1901.02029
Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements. Dictionary or transform learning methods are useful in applications such as denoising, inpainting, and medical image reconstru
Externí odkaz:
http://arxiv.org/abs/1809.01817
This work presents a new robust PCA method for foreground-background separation on freely moving camera video with possible dense and sparse corruptions. Our proposed method registers the frames of the corrupted video and then encodes the varying per
Externí odkaz:
http://arxiv.org/abs/1712.06229
Photometric stereo is a method that seeks to reconstruct the normal vectors of an object from a set of images of the object illuminated under different light sources. While effective in some situations, classical photometric stereo relies on a diffus
Externí odkaz:
http://arxiv.org/abs/1710.08873
This paper introduces a novel approach to robust surface reconstruction from photometric stereo normal vector maps that is particularly well-suited for reconstructing surfaces from noisy gradients. Specifically, we propose an adaptive dictionary lear
Externí odkaz:
http://arxiv.org/abs/1710.00230
Photometric stereo is a method for estimating the normal vectors of an object from images of the object under varying lighting conditions. Motivated by several recent works that extend photometric stereo to more general objects and lighting condition
Externí odkaz:
http://arxiv.org/abs/1710.00002