Open AccessSub‐population prediction using enhanced correlation filters
Author(s) -
Wang Hongfei,
He Kun
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.0338
Subject(s) - correlation , metric (unit) , computer science , population , filter (signal processing) , artificial intelligence , fourier transform , identification (biology) , pattern recognition (psychology) , data mining , algorithm , mathematics , computer vision , engineering , mathematical analysis , operations management , botany , demography , geometry , sociology , biology
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.