Dual-perspective multi-instance embedding learning with adaptive density distribution mining

Mei Yang, Tian-Lin Chen, Wei-Zhi Wu, Wen-Xi Zeng, Jing-Yu Zhang, Fan Min

Abstract: Highlights•We propose an adaptive density distribution mining for instance selection.•We propose a dual-perspective embedding technique to maintain the distinguishability.•We obtain the final ensemble model through weighted voting.•Our algorithm achieves SOTA performance in terms of accuracy.