A Perceptron-based Fine Approximation Technique for Linear Separation
Ákos Hajnal
This paper presents a novel online learning method that aims at finding a
separator hyperplane between data points labelled as either positive or
negative. Since weights and biases of artificial neurons can directly be
related to hyperplanes in high-dimensional spaces, the technique is applicable
to train perceptron-based binary classifiers in machine learning. In case of
large or imbalanced data sets, use of analytical or gradient-based solutions
can become prohibitive and impractical, where heuristics and approximation
techniques are still applicable. The proposed method is based on the Perceptron
algorithm, however, it tunes neuron weights in just the necessary extent during
searching the separator hyperplane. Due to an appropriate transformation of the
initial data set we need not to consider data labels, neither the bias term.
respectively, reducing separability to a one-class classification problem. The
presented method has proven converge; empirical results show that it can be
more efficient than the Perceptron algorithm, especially, when the size of the
data set exceeds data dimensionality.