Search Results for author:
Found 15 papers, 6 papers with code
On learning with shift-invariant structures
We describe new results and algorithms for two different, but related, problems which deal with circulant matrices: learning shift-invariant components from training data and calculating the shift (or alignment) between two given signals.
Approximate Eigenvalue Decompositions of Linear Transformations with a Few Householder Reflectors
The ability to decompose a signal in an orthonormal basis (a set of orthogonal components, each normalized to have unit length) using a fast numerical procedure rests at the heart of many signal processing methods and applications.
Learning Multiplication-free Linear Transformations
In this paper, we propose several dictionary learning algorithms for sparse representations that also impose specific structures on the learned dictionaries such that they are numerically efficient to use: reduced number of addition/multiplications and even avoiding multiplications altogether.
Efficient and Parallel Separable Dictionary Learning
Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images.
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection
In this paper we use a particular DL formulation that seeks uniform sparse representations model to detect the underlying subspace of the majority of samples in a dataset, using a K-SVD-type algorithm.
Learning Fast Sparsifying Transforms
We also propose a method to construct fast square but non-orthogonal dictionaries that are factorized as a product of few transforms that can be viewed as a further generalization of Givens rotations to the non-orthogonal setting.
Fast Orthonormal Sparsifying Transforms Based on Householder Reflectors
Dictionary learning is the task of determining a data-dependent transform that yields a sparse representation of some observed data.
Fast approximation of orthogonal matrices and application to PCA
We study the problem of approximating orthogonal matrices so that their application is numerically fast and yet accurate.
Constructing fast approximate eigenspaces with application to the fast graph Fourier transforms
We investigate numerically efficient approximations of eigenspaces associated to symmetric and general matrices.
A Note on Shift Retrieval Problems
In this note, we discuss the shift retrieval problems, both classical and compressed, and provide connections between them using circulant matrices.
An iterative coordinate descent algorithm to compute sparse low-rank approximations
In this paper, we describe a new algorithm to build a few sparse principal components from a given data matrix.
Low complexity joint position and channel estimation at millimeter wave based on multidimensional orthogonal matching pursuit
Compressive approaches provide a means of effective channel high resolution channel estimates in millimeter wave MIMO systems, despite the use of analog and hybrid architectures.
Multidimensional orthogonal matching pursuit: theory and application to high accuracy joint localization and communication at mmWave
Greedy approaches in general, and orthogonal matching pursuit in particular, are the most commonly used sparse recovery techniques in a wide range of applications.
Kernel t-distributed stochastic neighbor embedding
This paper presents a kernelized version of the t-SNE algorithm, capable of mapping high-dimensional data to a low-dimensional space while preserving the pairwise distances between the data points in a non-Euclidean metric.
Learning Explicitly Conditioned Sparsifying Transforms
Sparsifying transforms became in the last decades widely known tools for finding structured sparse representations of signals in certain transform domains.
