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Fast orthogonal forward selection algorithm for feature subset selection.

¹Sch. of Electr. and Electron. Eng., Nanyang Technol. Univ., Singapore.

IEEE Transactions on Neural Networks

|February 5, 2008

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We introduce a fast orthogonal forward selection (FOFS) algorithm for efficient feature subset selection in pattern classification. This method reduces computational cost compared to traditional orthogonal forward selection (OFS).

Area of Science:

Computer Science
Machine Learning
Pattern Recognition

Background:

Feature selection is crucial for optimizing pattern classification models.
High dimensionality and feature correlations pose computational challenges.

Purpose of the Study:

To develop a computationally efficient algorithm for feature subset selection.
To introduce the Fast Orthogonal Forward Selection (FOFS) algorithm.

Main Methods:

The FOFS algorithm utilizes an implicit orthogonal transform to handle feature correlations.
It decomposes correlations among candidate features without explicit computation.

Main Results:

FOFS achieves significant reductions in computational effort compared to conventional Orthogonal Forward Selection (OFS).

Related Experiment Videos

The implicit decomposition enhances processing speed for feature selection.

Conclusions:

The FOFS algorithm offers a faster and more efficient approach to feature subset selection.
This method is beneficial for large-scale pattern classification tasks.