39 citations · 87 across the 16 of their papers we have counts for
19 papers
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…
Multidimensional orthogonal matching pursuit: theory and application to high accuracy joint localization and communication at mmWave
Joan Palacios, Nuria González-Prelcic, Cristian Rusu
Greedy approaches in general, and orthogonal matching pursuit in particular, are the most commonly used sparse recovery techniques in a wide range of applications. The complexity o…
Low complexity joint position and channel estimation at millimeter wave based on multidimensional orthogonal matching pursuit
Joan Palacios, Nuria González-Prelcic, Cristian Rusu
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. S…
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…
An iterative Jacobi-like algorithm to compute a few sparse approximate eigenvectors
Cristian Rusu
In this paper, we describe a new algorithm that approximates the extreme eigenvalue/eigenvector pairs of a symmetric matrix. The proposed algorithm can be viewed as an extension of…
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…
Fast approximation of orthogonal matrices and application to PCA
Cristian Rusu, Lorenzo Rosasco
We study the problem of approximating orthogonal matrices so that their application is numerically fast and yet accurate. We find an approximation by solving an optimization proble…
A Note on Shift Retrieval Problems
Cristian Rusu
In this note, we discuss the shift retrieval problems, both classical and compressed, and provide connections between them using circulant matrices. We review the properties of cir…
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…
Approximate Eigenvalue Decompositions of Linear Transformations with a Few Householder Reflectors
Cristian Rusu
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…
Algorithms for the Construction of Incoherent Frames Under Various Design Constraints
Cristian Rusu, Nuria Gonzalez-Prelcic, Robert W. Heath
Unit norm finite frames are generalizations of orthonormal bases with many applications in signal processing. An important property of a frame is its coherence, a measure of how cl…
Sensor scheduling with time, energy and communication constraints
Cristian Rusu, John Thompson, Neil M. Robertson
In this paper we present new algorithms and analysis for the linear inverse sensor placement and scheduling problems over multiple time instances with power and communications cons…
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…
Hybrid MIMO Architectures for Millimeter Wave Communications: Phase Shifters or Switches?
Roi Méndez-Rial, Cristian Rusu, Nuria González-Prelcic +2
Hybrid analog/digital MIMO architectures were recently proposed as an alternative for fully-digitalprecoding in millimeter wave (mmWave) wireless communication systems. This is mot…
Optimized Compressed Sensing via Incoherent Frames Designed by Convex Optimization
Cristian Rusu, Nuria González-Prelcic
The construction of highly incoherent frames, sequences of vectors placed on the unit hyper sphere of a finite dimensional Hilbert space with low correlation between them, has prov…