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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Conformation-based hidden Markov models: application to human face identification.

Djamel Bouchaffra¹

¹Mathematics and Computer Science Department, Grambling State University, Grambling, LA 71245, USA. dbouchaffra@ieee.org

IEEE Transactions on Neural Networks

|February 23, 2010

Summary

This summary is machine-generated.

Conformation-based Hidden Markov Models (COHMMs) address limitations in traditional models by incorporating shape information. This novel approach enhances sequence classification, particularly for complex data like human faces.

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Area of Science:

Computer Science
Pattern Recognition
Machine Learning

Background:

Hidden Markov Models (HMMs) are effective for classifying structured objects.
A key limitation of HMMs is their inability to intrinsically handle shape or conformation of visible observation (VO) sequences.
Predicting the n-dimensional shape formed by VO sequences remains a challenge for standard HMMs.

Purpose of the Study:

To introduce a novel paradigm, Conformation-based Hidden Markov Models (COHMMs), to address the shape-related limitations of HMMs.
To develop a formalism capable of classifying VO sequences by considering their intrinsic shape.
To extend the COHMM framework to both one-level and multilevel structures.

Main Methods:

COHMMs embed nodes of an HMM state transition graph within a Euclidean vector space.
The method models noise within the shape formed by the VO sequence.
A multilevel COHMM framework is presented, addressing sequence probability, statistical and structural decoding, shape decoding, and learning.

Main Results:

The proposed COHMM formalism was applied to human face identification tasks.
Experiments were conducted on various benchmarked face databases.
Multilevel COHMMs demonstrated superior performance compared to embedded HMMs and other standard HMM-based models.

Conclusions:

COHMMs offer a significant advancement in sequence classification by integrating conformational information.
The multilevel COHMM approach is particularly effective for complex tasks such as human face identification.
This formalism provides a robust solution for analyzing and classifying structured data where shape is a critical feature.