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access icon free Sub-population prediction using enhanced correlation filters

Minimum average correlation energy (MACE) filters are initially developed and widely used for image pattern recognition tasks. A novel method leveraging the enhanced MACE filters is proposed to tackle classification problems from a new perspective. By employing 1D Fourier transform, establishing new identification metric, and improving numerical stability, the proposed method constructs an enhanced correlation filter to select a sub-population of the un-labelled data, and subsequently outputs labels. Experiments show our method achieves 100% precision on multiple datasets considered, including two public benchmarks and one obtained from semiconductor industry addressing an emerging task.

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http://iet.metastore.ingenta.com/content/journals/10.1049/el.2018.0338
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