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A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

In this paper, we prove a new identity for the least-square solution of an over-determined set of linear equation

Ax=b

, where

A

is an

m\times n

full-rank matrix,

b

is a column-vector of dimension

m

, and

m

(the number of equations) is larger tha ...

2015

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

In this paper, we prove a new identity for the least-square solution of an over-determined set of linear equation

Ax=b

, where

A

is an

m\times n

full-rank matrix,

b

is a column-vector of dimension

m

, and

m

(the number of equations) is larger tha ...

Idiap2015

From averaging to acceleration, there is only a step-size

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Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Forecasting Uncertainty in Electricity Demand

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

MATHICSE Technical Report : Discrete least squares polynomial approximation with random evaluations – application to parametric and stochastic elliptic PDES

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Forecasting Uncertainty in Electricity Demand

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

MATHICSE Technical Report : Discrete least squares polynomial approximation with random evaluations – application to parametric and stochastic elliptic PDES