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.
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Acknowledgements
We are grateful to Meir Feder (Tel Aviv University) for his support and comments and to Yossi Keller (Bar Ilan University) for the provision of ImageNet data.
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Nissani (Nissensohn), D.N. (2019). An Unsupervised Learning Classifier with Competitive Error Performance. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_29
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DOI: https://doi.org/10.1007/978-3-030-13709-0_29
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