机构地区: 南京航空航天大学计算机科学与技术学院,南京211106
出 处: 《模式识别与人工智能》 2017年第8期740-746,共7页
摘 要: 受几何平均度量学习(GMML)方法启发,文中提出凸判别型典型相关分析(CDCA).CDCA将学习2个视图的投影矩阵转化为一个测地线凸的度量学习问题,获得一个全局的闭合解,同时直接获得判别性融合特征.在人工数据集和真实数据集上通过实验验证CDCA的有效性. Inspired by geometric mean metric learning (GMML), a convex discriminant canonical correlation analysis (CDCA) is proposed. The learning of two projection matrices is transformed into a geodesic convex problem of metric learning. Thereby a closed form solution is acquired and simultaneously discriminant fused features are extracted directly. The experiments on artificial and real datasets verify the effectiveness of CDCA.