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Found 10 papers, 1 papers with code
Precoding for High Throughput Satellite Communication Systems: A Survey
This survey presents an overview and a classification of the recent precoding techniques for HTS communication systems from two main perspectives: 1) a problem formulation perspective and 2) a system design perspective.
Sharp Analysis of RLS-based Digital Precoder with Limited PAPR in Massive MIMO
We show that for this class of precoders, there is an optimal transmit per-antenna power that maximizes the system performance in terms of SINAD and bit error probability.
Weight Vector Tuning and Asymptotic Analysis of Binary Linear Classifiers
Applying this method to a number of linear classifiers under a variety of data dimensionality and sample size settings reveals that the classification performance loss due to non-optimal native hyperparameters can be compensated for by weight vector tuning.
A Precise Performance Analysis of Support Vector Regression
In this paper, we study the hard and soft support vector regression techniques applied to a set of $n$ linear measurements of the form $y_i=\boldsymbol{\beta}_\star^{T}{\bf x}_i +n_i$ where $\boldsymbol{\beta}_\star$ is an unknown vector, $\left\{{\bf x}_i\right\}_{i=1}^n$ are the feature vectors and $\left\{{n}_i\right\}_{i=1}^n$ model the noise.
High-Dimensional Quadratic Discriminant Analysis under Spiked Covariance Model
Quadratic discriminant analysis (QDA) is a widely used classification technique that generalizes the linear discriminant analysis (LDA) classifier to the case of distinct covariance matrices among classes.
Improved Design of Quadratic Discriminant Analysis Classifier in Unbalanced Settings
The use of quadratic discriminant analysis (QDA) or its regularized version (R-QDA) for classification is often not recommended, due to its well-acknowledged high sensitivity to the estimation noise of the covariance matrix.
Asymptotic Analysis of an Ensemble of Randomly Projected Linear Discriminants
Datasets from the fields of bioinformatics, chemometrics, and face recognition are typically characterized by small samples of high-dimensional data.
A Model of Double Descent for High-dimensional Binary Linear Classification
We consider a model for logistic regression where only a subset of features of size $p$ is used for training a linear classifier over $n$ training samples.
Risk Convergence of Centered Kernel Ridge Regression with Large Dimensional Data
This paper carries out a large dimensional analysis of a variation of kernel ridge regression that we call \emph{centered kernel ridge regression} (CKRR), also known in the literature as kernel ridge regression with offset.
A Large Dimensional Study of Regularized Discriminant Analysis Classifiers
This article carries out a large dimensional analysis of standard regularized discriminant analysis classifiers designed on the assumption that data arise from a Gaussian mixture model with different means and covariances.
