Regularized Partial Least Squares with an Application to NMR Spectroscopy

Details Regularized Partial Least Squares with an Application to NMR Spectroscopy Regularized Partial Least Squares with an Application to NMR Spectroscopy

2012-04-17

arxiv.org/abs/1204.3942v1

Statistics

Machine Learning

Scientific paper

High-dimensional data common in genomics, proteomics, and chemometrics often contains complicated correlation structures. Recently, partial least squares (PLS) and Sparse PLS methods have gained attention in these areas as dimension reduction techniques in the context of supervised data analysis. We introduce a framework for Regularized PLS by solving a relaxation of the SIMPLS optimization problem with penalties on the PLS loadings vectors. Our approach enjoys many advantages including flexibility, general penalties, easy interpretation of results, and fast computation in high-dimensional settings. We also outline extensions of our methods leading to novel methods for Non-negative PLS and Generalized PLS, an adaption of PLS for structured data. We demonstrate the utility of our methods through simulations and a case study on proton Nuclear Magnetic Resonance (NMR) spectroscopy data.

Affiliated with

Also associated with

No associations

Rating

Regularized Partial Least Squares with an Application to NMR Spectroscopy does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Regularized Partial Least Squares with an Application to NMR Spectroscopy, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Regularized Partial Least Squares with an Application to NMR Spectroscopy will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-410822