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Related Experiment Video

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A Tactile Automated Passive-Finger Stimulator TAPS

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Published on: June 3, 2009

BAYESIAN WAVELET-BASED CURVE CLASSIFICATION VIA DISCRIMINANT ANALYSIS WITH MARKOV RANDOM TREE PRIORS.

Francesco C Stingo¹, Marina Vannucci², Gerard Downey³

¹Department of Statistics, Rice University, Houston, TX 77251, U.S.A. fcs1@rice.edu.

Statistica Sinica

|April 25, 2014

Summary

This study introduces a new method for classifying food types using wavelet transforms and Bayesian discriminant analysis. The approach effectively identifies food species from near-infrared spectroscopy data, even with many variables.

Keywords:

Bayesian variable selection Classification and pattern recognition Markov chain Monte Carlo Markov random tree prior Wavelet-based modeling

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Chemometrics
Statistical Learning
Spectroscopy

Background:

Discriminant analysis is crucial for classifying data but struggles with high-dimensional datasets where variables exceed observations.
Dimension reduction is essential for effective classification in such scenarios.
Food authenticity analysis using spectroscopy presents a common challenge in chemometrics.

Purpose of the Study:

To develop a robust classification method for high-dimensional spectroscopic data.
To apply a nonparametric approach using wavelet transforms combined with discriminant analysis.
To address the challenge of classifying food species using near-infrared spectroscopy data.

Main Methods:

Functional predictors modeled using wavelet transforms.
Bayesian conjugate normal discriminant models (linear and quadratic) applied in the wavelet domain.
Latent binary indicators and Markov random tree (MRT) priors for selecting discriminatory wavelet coefficients.
Markov Chain Monte Carlo (MCMC) methods for posterior inference.

Main Results:

Demonstrated effectiveness in a case study on food authenticity.
Achieved accurate classification of different food species.
Outperformed several other existing classification procedures.

Conclusions:

The proposed wavelet-based Bayesian discriminant analysis is a powerful tool for high-dimensional chemometric classification.
This nonparametric approach effectively handles complex spectroscopic data for food authenticity.
The method provides a robust alternative to traditional discriminant analysis in challenging scenarios.