25 citations · 85 across the 15 of their papers we have counts for
17 papers
Probabilistic Position-Aided Beam Selection for mmWave MIMO Systems
Joseph K. Chege, Arie Yeredor, Martin Haardt
Millimeter-wave (mmWave) MIMO systems rely on highly directional beamforming to overcome severe path loss and ensure robust communication links. However, selecting the optimal beam…
A Unified MDL-based Binning and Tensor Factorization Framework for PDF Estimation
Mustafa Musab, Joseph K. Chege, Arie Yeredor +1
Reliable density estimation is fundamental for numerous applications in statistics and machine learning. In many practical scenarios, data are best modeled as mixtures of component…
Joint Bayesian Parameter and Model Order Estimation for Low-Rank Probability Mass Tensors
Joseph K. Chege, Arie Yeredor, Martin Haardt
Obtaining a reliable estimate of the joint probability mass function (PMF) of a set of random variables from observed data is a significant objective in statistical signal processi…
Time-Domain Based Embeddings for Spoofed Audio Representation
Matan Karo, Arie Yeredor, Itshak Lapidot
Anti-spoofing is the task of speech authentication. That is, identifying genuine human speech compared to spoofed speech. The main focus of this paper is to suggest new representat…
Monotonicity of the Trace-Inverse of Covariance Submatrices and Two-Sided Prediction
Anatoly Khina, Arie Yeredor, Ram Zamir
It is common to assess the "memory strength" of a stationary process looking at how fast the normalized log-determinant of its covariance submatrices (i.e., entropy rate) decreases…
Enhanced Blind Calibration of Uniform Linear Arrays with One-Bit Quantization by Kullback-Leibler Divergence Covariance Fitting
Amir Weiss, Arie Yeredor
One-bit quantization has recently become an attractive option for data acquisition in cutting edge applications, due to the increasing demand for low power and higher sampling rate…
Non-Iterative Blind Calibration of Nested Arrays with Asymptotically Optimal Weighting
Amir Weiss, Arie Yeredor
Blind calibration of sensors arrays (without using calibration signals) is an important, yet challenging problem in array processing. While many methods have been proposed for "cla…
Iterative Symbol Recovery For Power Efficient DC Biased Optical OFDM Systems
Amir Weiss, Arie Yeredor, Mark Shtaif
Orthogonal frequency division multiplexing (OFDM) has proven itself as an effective multi-carrier digital communication technique. In recent years the interest in optical OFDM has…
The Extended "Sequentially Drilled" Joint Congruence Transformation and its Application in Gaussian Independent Vector Analysis
Amir Weiss, Arie Yeredor, Sher Ali Cheema +1
Independent Vector Analysis (IVA) has emerged in recent years as an extension of Independent Component Analysis (ICA) into multiple sets of mixtures, where the source signals in ea…
Performance Analysis of the Gaussian Quasi-Maximum Likelihood Approach for Independent Vector Analysis
Amir Weiss, Sher Ali Cheema, Martin Haardt +1
Maximum Likelihood (ML) estimation requires precise knowledge of the underlying statistical model. In Quasi ML (QML), a presumed model is used as a substitute to the (unknown) true…
Blind Determination of the Number of Sources Using Distance Correlation
Amir Weiss, Arie Yeredor
A novel blind estimate of the number of sources from noisy, linear mixtures is proposed. Based on Székely et al.'s distance correlation measure, we define the Sources' Dependency C…
Asymptotically Optimal Blind Calibration of Uniform Linear Sensor Arrays for Narrowband Gaussian Signals
Amir Weiss, Arie Yeredor
An asymptotically optimal blind calibration scheme of uniform linear arrays for narrowband Gaussian signals is proposed. Rather than taking the direct Maximum Likelihood (ML) appro…
A Maximum Likelihood-Based Minimum Mean Square Error Separation and Estimation of Stationary Gaussian Sources from Noisy Mixtures
Amir Weiss, Arie Yeredor
In the context of Independent Component Analysis (ICA), noisy mixtures pose a dilemma regarding the desired objective. On one hand, a "maximally separating" solution, providing the…
First-Order Perturbation Analysis of the SECSI Framework for the Approximate CP Decomposition of 3-D Noise-Corrupted Low-Rank Tensors
Sher Ali Cheema, Emilio Rafael Balda, Yao Cheng +3
The Semi-Algebraic framework for the approximate Canonical Polyadic (CP) decomposition via SImultaneaous matrix diagonalization (SECSI) is an efficient tool for the computation of…
Kernel Scaling for Manifold Learning and Classification
Ofir Lindenbaum, Moshe Salhov, Arie Yeredor +1
Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the…
MultiView Diffusion Maps
Ofir Lindenbaum, Arie Yeredor, Moshe Salhov +1
In this paper, we address the challenging task of achieving multi-view dimensionality reduction. The goal is to effectively use the availability of multiple views for extracting a…
Independent Component Analysis Over Galois Fields
Arie Yeredor
We consider the framework of Independent Component Analysis (ICA) for the case where the independent sources and their linear mixtures all reside in a Galois field of prime order P…