39 citations · 87 across the 16 of their papers we have counts for
9 papers · 1 filter
Kernel t-distributed stochastic neighbor embedding
Denis C. Ilie-Ablachim, Bogdan Dumitrescu, Cristian Rusu
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…
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection
Paul Irofti, Cristian Rusu, Andrei Pătraşcu
Many applications like audio and image processing show that sparse representations are a powerful and efficient signal modeling technique. Finding an optimal dictionary that genera…
An iterative coordinate descent algorithm to compute sparse low-rank approximations
Cristian Rusu
In this paper, we describe a new algorithm to build a few sparse principal components from a given data matrix. Our approach does not explicitly create the covariance matrix of the…
Efficient and Parallel Separable Dictionary Learning
Cristian Rusu, Paul Irofti
Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learn…
Constructing fast approximate eigenspaces with application to the fast graph Fourier transforms
Cristian Rusu, Lorenzo Rosasco
We investigate numerically efficient approximations of eigenspaces associated to symmetric and general matrices. The eigenspaces are factored into a fixed number of fundamental com…
Learning Multiplication-free Linear Transformations
Cristian Rusu
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 nume…
On learning with shift-invariant structures
Cristian Rusu
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 calcu…
Fast Orthonormal Sparsifying Transforms Based on Householder Reflectors
Cristian Rusu, Nuria Gonzalez-Prelcic, Robert Heath
Dictionary learning is the task of determining a data-dependent transform that yields a sparse representation of some observed data. The dictionary learning problem is non-convex,…
Learning Fast Sparsifying Transforms
Cristian Rusu, John Thompson
Given a dataset, the task of learning a transform that allows sparse representations of the data bears the name of dictionary learning. In many applications, these learned dictiona…