The structural resemblance among several existing classifiers has motivated us to investigate their underlying relationships. By exploring into the mapping solutions of these classifiers, we found that they can be linked by simple feature data scaling. In other words, the key to these relationships lies upon how the replica of feature data are being scaled. This finding leads us directly to an exploration of novel classifiers beyond existing settings. Based on an extensive empirical evaluation, we show that the proposed formulation facilitates a tuning capability beyond existing settings for classifier generalization.
Bibliographical noteFunding Information:
The authors thank Professor Anil K. Jain (MSU) for his kind suggestions and discussions to improve the paper. The proof readings of several mathematical formulations by Professor Lei Sun, Professor Haiping Lu, Professor Zhiping Lin and Professor Andrew Teoh are also appreciated. Finally we acknowledge the anonymous reviewers for their constructive comments. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant number: NRF-2012R1A1A2042428).
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence