k-nearest neighbors directed noise injection in multilayer perceptron training

Area of Science:

• Machine Learning
• Statistical Classification
• Discriminant Analysis

Background:

• Classifier complexity and learning set size are critical in discriminant analysis.
• Adding noise to training data is a method to manage classifier complexity by increasing the training set size.
• The effectiveness of noise injection depends on its amount and direction.

Purpose of the Study:

• To investigate the impact of Gaussian spherical noise and k-nearest neighbors directed noise on multilayer perceptron performance.
• To theoretically analyze statistical classifiers due to the analytical intractability of multilayer perceptrons.
• To enhance understanding of how noise injection affects the accuracy of sample-based classifiers.

Main Methods:

• Empirical studies on multilayer perceptrons.
• Theoretical analysis of statistical classifiers.
• Comparison of Gaussian spherical noise versus k-nearest neighbors directed noise injection.

Main Results:

• k-nearest neighbors directed noise injection is more effective than Gaussian spherical noise injection.
• The preference for k-nearest neighbors directed noise is particularly evident in data with low intrinsic dimensionality.
• Both empirical and theoretical findings support the superiority of directed noise injection.

Conclusions:

• Noise injection is a viable strategy for improving classifier training and performance.
• k-nearest neighbors directed noise offers significant advantages over Gaussian spherical noise for specific data characteristics.
• Understanding noise injection effects is crucial for optimizing sample-based classification accuracy.