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We present a tuning-free and computationally simple solution for multivariate reduced rank regression, based on the recently introduced signal subspace matching (SSM) metric. Unlike the existing solutions, which solve simultaneously for the rank ...

• A tuning-free and low-complexity solution to the problem of reduced rank regression.
• The solution decouples the rank selection and coefficient matrix estimation into two stages.
• The rank is determined using the signal subspace ...

We present a novel solution to the problem of subspace outlier detection that does not assume prior knowledge of the number of outliers nor the dimension of the inliers subspace. The solution is based on the recently introduced notion of soft projection ...

We present a new spectral framework to design and optimise numerical methods for convection problems termed Local Transfer function Analysis (LTA), which improves the state-of-art model for error dynamics due to numerical discretisation. LTA ...

We present a novel and computationally simple solution to the problem of determining the order of finite impulse response (FIR) single-input multiple-output (SIMO) channel. The solution is applicable to ideal and realistic wireless ...

We present a powerful solution to the problem of vector set classification, based on a novel goodness-of-fit metric, referred to as signal subspace matching (SSM). Unlike the existing solutions based on principal component analysis (PCA), this solution is ...

The massive spread of hate speech, hateful content targeted at specific subpopulations, is a problem of critical social importance. Automated methods of hate speech detection typically employ state-of-the-art deep learning (DL)-based text ...

• Present compact character-based HyperNetworks for hate speech detection.
• These networks are trained using large-scale amounts of generated data.
• Performance is competitive and sometimes exceeds large pretrained language models.

We present a novel and computationally simple solution to the problem of detecting the number of signals, which is applicable to both white and colored noise, and to a very small number of samples. The solution is based on a novel and non-asymptotic ...

We present a novel solution to the problem of estimating the array response of the signal of interest (SOI) in case it is constrained to lie in a known subspace, aimed at coping with model errors in the known subspace. The solution is based on a novel ...

• A novel convex-optimization-based low-rank approximation approach to the solutions of a family of problems involving constant modulus signals.

We present a novel convex-optimization-based approach to the solutions of a family of problems involving constant modulus signals. The family of problems includes the constant modulus and the constrained constant modulus, as well as ...

We consider the problem of simultaneous sparse coding and anomaly detection in a collection of data vectors. The majority of the data vectors are assumed to conform with a sparse representation model, whereas the anomaly is caused by an unknown subset ...

We propose the audio inpainting framework that recovers portions of audio data distorted due to impairments such as impulsive noise, clipping, and packet loss. In this framework, the distorted data are treated as missing and their location is assumed to ...

We present a novel approach for online shrinkage functions learning in single image super-resolution. The proposed approach leverages the classical Wavelet Shrinkage denoising technique where a set of scalar shrinkage functions is applied to the wavelet ...

A pattern database (PDB) is a heuristic function in a form of a lookup table which stores the cost of optimal solutions for instances of subproblems. These subproblems are generated by abstracting the entire search space into a smaller space called the ...