计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211000131-6.doi: 10.11896/jsjkx.211000131
任双艳1, 郭威1, 范昌琪2, 王喆1, 吴松洋3
REN Shuang-yan1, GUO Wei1, FAN Chang-qi2, WANG Zhe1, WU Song-yang3
摘要: 几何信息可以为分类方法提供先验知识和直观解释。从几何角度观察样本是一种新的样本学习方法,密度则是几何信息中非常直观的表现形式。提出了基于类间和类内密度的多视角距离度量学习方法来学习一个度量空间。在这个空间内,异类样本更加分散,同类样本更加紧密。首先,在大边际框架下引入类间密度,通过最小化类间密度来约束度量空间中的样本,从而实现类间分散,提高分类性能。其次,引入类内密度,通过最大化类内密度来达到同类样本互相靠近的效果,从而实现类内紧凑。最后,为了更好地挖掘多视角样本的互补信息,最大化度量空间中各视角之间的相关性,使各视角自适应地相互学习,探索视角之间的互补信息。在真实数据集上的大量实验结果证明了该方法的优越性。
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