genpranav/l1-norm-problems-using-admm

Least Absolute Deviation(LAD), Huber Fitting and Least Absolute Shrinkage and Selection Operator(LASSO) solved using Alternating Direction Method of Multipliers(ADMM) approach, from a Linear Algebra standpoint along with the Machine Learning justification for exploration.

L1-Norm_Problems_using_ADMM

Least Absolute Deviation(LAD), Huber Fitting and Least Absolute Shrinkage and Selection Operator(LASSO) solved using Alternating Direction Method of Multipliers(ADMM) approach, from a Linear Algebra standpoint along with the Machine Learning justification for exploration.

Documentation

The Documentation of this project - click here

Run Locally

As of now only local deployment is possible and the corresponding files for,

Feauture selector result comparions with individual feature correlation in python - here

Loss plots in matlab for,

LAD and LS - here

L1, L2, Huber - here

Update equation iterations in matlab - here

Contributors

B.E.Pranav Kumaar

Student ID @Amrita Vishwa Vidyapeetham - CB.EN.U4AIE20052

🔥 twitter

⚡ LinkedIn

❄️ Github

Divi Eswar Choudary

Student ID @Amrita Vishwa Vidyapeetham - CB.EN.U4AIE20012

🔥 twitter

⚡ LinkedIn

❄️ Github

Rishekesan

Student ID @Amrita Vishwa Vidyapeetham - CB.EN.U4AIE20058

Dabbra Harsha

Student ID @Amrita Vishwa Vidyapeetham - CB.EN.U4AIE20010