Title: An Unsupervised Learning Classifier with Competitive Error Performance,
Abstract: An unsupervised learning classification model is described. It achieves
classification error probability competitive with that of popular supervised
learning classifiers such as SVM or kNN. The model is based on the incremental
execution of small step shift and rotation operations upon selected
discriminative hyperplanes at the arrival of input samples. When applied, in
conjunction with a selected feature extractor, to a subset of the ImageNet
dataset benchmark, it yields 6.2 % Top 3 probability of error; this exceeds by
merely about 2 % the result achieved by (supervised) k-Nearest Neighbor, both
using same feature extractor. This result may also be contrasted with popular
unsupervised learning schemes such as k-Means which is shown to be practically
useless on same dataset.