performing a least squares with regularisation in matlab
32 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
I have data sets X (2n by 8) and Y(2n by 1). I want to find the coefficients a so that Y = Xa. So we can perform a = X\Y (as a least squares minimisation).
I wanted to ask if it possible to proceed with a form of regularisation (L1 or something simple) from this?
Please help.
0 Kommentare
Antworten (2)
Diwakar
am 13 Jul. 2018
My understanding of your problem is that you want to find the coefficient a. So in order to implement optimization you can implement average of sum of least squares as shown below.
Loss= ((Y-X*a)'*(Y-X*a))/(2*n);
The above shown function is a vectorized implementation of the squared error loss function. So this can be minimized in order to get the optimal value of a. If you want to fit a curve to this then any form of regularization should be fine.
Hope this helps
Cheers!
1 Kommentar
Bruno Luong
am 23 Sep. 2020
Bearbeitet: Bruno Luong
am 23 Sep. 2020
Simpless method:
n = size(X,2); % 8
lambda = 1e-6; % <= regularization parameter, 0 no regularization, larger value stronger regularized solution
a = [X; lambda*eye(n)] \ [Y; zeros(n,1)]
1 Kommentar
Siehe auch
Kategorien
Mehr zu Problem-Based Optimization Setup finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!