Related Experiment Video
Updated: Aug 11, 2026

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
Published on: August 19, 2021
Unsupervised feature dimension reduction for classification of MR spectra
R Baumgartner1, R Somorjai, C Bowman
1Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Canada.
Abstract:
We present an unsupervised feature dimension reduction method for the classification of magnetic resonance spectra. The technique preserves spectral information, important for disease profiling. We propose to use this technique as a preprocessing step for computationally demanding wrapper-based feature subset selection. We show that the classification accuracy on an independent test set can be sustained while achieving considerable feature reduction. Our method is applicable to other classification techniques, such as neural networks, support vector machines, etc.
Related Concept Videos
¹H NMR: Interpreting Distorted and Overlapping Signals
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are slanted or...
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)
Two-Dimensional (2D) NMR: Overview
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse.
High-Resolution Mass Spectrometry (HRMS)
¹³C NMR: ¹H–¹³C Decoupling
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
IR Frequency Region: Fingerprint Region
The...

