An Unsupervised Learning Classifier with Competitive Error Performance
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An unsupervised learning classification model is described. It achieves classification error probability competitive with that of state-of-the-art 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 merely about 2 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.
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