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Found 14 papers, 2 papers with code
Training Data Size Induced Double Descent For Denoising Neural Networks and the Role of Training Noise Level
In fact the generalization error versus number of of training data points is a double descent curve.
Sparse Equisigned PCA: Algorithms and Performance Bounds in the Noisy Rank-1 Setting
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 samples.
Free Component Analysis: Theory, Algorithms & Applications
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.
Time Series Source Separation using Dynamic Mode Decomposition
We show that when the latent time series are uncorrelated at a lag of one time-step then, in the large sample limit, the recovered dynamic modes will approximate, up to a column-wise normalization, the columns of the mixing matrix.
Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models
Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements.
Panoramic Robust PCA for Foreground-Background Separation on Noisy, Free-Motion Camera Video
We perform extensive numerical experiments on both static and moving camera video subject to a variety of dense and sparse corruptions.
Robust Photometric Stereo via Dictionary Learning
We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertian reflectance model.
Robust Surface Reconstruction from Gradients via Adaptive Dictionary Regularization
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.
Robust Photometric Stereo Using Learned Image and Gradient Dictionaries
Photometric stereo is a method for estimating the normal vectors of an object from images of the object under varying lighting conditions.
Augmented Robust PCA For Foreground-Background Separation on Noisy, Moving Camera Video
This work presents a novel approach for robust PCA with total variation regularization for foreground-background separation and denoising on noisy, moving camera video.
Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging
For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements.
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The $\ell_0$ Method
The proposed block coordinate descent algorithm involves efficient closed-form solutions.
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems
This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns.
OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage
The truncated singular value decomposition (SVD) of the measurement matrix is the optimal solution to the_representation_ problem of how to best approximate a noisy measurement matrix using a low-rank matrix.
