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Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

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Exponential local discriminant embedding and its application to face recognition.

Fadi Dornaika¹, Alireza Bosaghzadeh

¹Department of Computer Science and Artificial Intelligence, University of Basque Country (UPV/EHU), 20018 San Sebastian, Spain. fdornaika@hotmail.fr

IEEE Transactions on Cybernetics

|November 13, 2012

Summary

This summary is machine-generated.

Exponential Local Discriminant Embedding (ELDE) addresses the small-sample-size problem in high-dimensional data. This novel discriminant technique enhances classification accuracy by transforming data before applying Local Discriminant Embedding.

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Area of Science:

Computer Science
Machine Learning
Pattern Recognition

Background:

Local Discriminant Embedding (LDE) improves upon global linear discriminant analysis.
LDE faces limitations with high-dimensional data and small training sets (small-sample-size problem).
Existing solutions often involve discarding discriminant information during dimensionality reduction.

Purpose of the Study:

Introduce Exponential Local Discriminant Embedding (ELDE) to overcome the small-sample-size problem in LDE.
Enhance classification accuracy by preserving discriminant information and enlarging inter-class margins.
Provide a novel discriminant technique that extends the LDE framework.

Main Methods:

Proposed Exponential Local Discriminant Embedding (ELDE) as an extension of the LDE framework.
ELDE overcomes the small-sample-size problem without losing discriminant information from null spaces.
Data is transformed via distance diffusion mapping into a new space before applying LDE.

Main Results:

ELDE effectively handles high-dimensional data with small training sets.
The distance diffusion mapping in ELDE enlarges the margin between different classes, improving classification.
Experiments on five public face databases (Yale, Extended Yale, PF01, PIE, FERET) demonstrate superior performance.

Conclusions:

ELDE offers a robust solution to the small-sample-size problem in discriminant analysis.
The proposed method outperforms existing LDE and other state-of-the-art discriminant techniques.
ELDE shows significant potential for improving face recognition and other classification tasks.