Abstract
We present a novel formulation for quasi-supervised learning that extends the learning paradigm to large datasets. Quasi-supervised learning computes the posterior probabilities of overlapping datasets at each sample and labels those that are highly specific to their respective datasets. The proposed formulation partitions the data into sample groups to compute the dataset posterior probabilities in a smaller computational complexity. In experiments on synthetic as well as real datasets, the proposed algorithm attained significant reduction in the computation time for similar recognition performances compared to the original algorithm, effectively generalizing the quasi-supervised learning paradigm to applications characterized by very large datasets.
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Acknowledgments
This work was supported by the European Union Seventh Framework Programme Marie Curie Action grant PIRG03-GA-2008-230903. The MiniBooNE neutrino dataset was provided by Dr. Byron Roe, Emeritus Professor at the Department of Physics, University of Michigan at Ann Arbor.
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Karaçalı, B. An efficient algorithm for large-scale quasi-supervised learning. Pattern Anal Applic 19, 311–323 (2016). https://doi.org/10.1007/s10044-014-0401-y
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DOI: https://doi.org/10.1007/s10044-014-0401-y