CAJ | 학술논문

多视图学习方法通过视图间互补信息的融合,达到增强单一视图方法的鲁棒性并提升学习性能的目的。典型相关分析(canonical correlation analysis,简称CCA)是一种重要的多视图信息融合技术。其研究的是针对同一组目标两组不同观测数据间的相关性,目标是得到一组相关性最大的投影向量。但当面对标号有序的分类任务时,CCA因没有利用类信息和类间有序信息,造成了对分类性能的制约。为此,通过将有序类信息嵌入 CCA 进行扩展,发展出有序判别典型相关分析(ordinal discriminative canonical correlation analysis,简称 OR-DisCCA)。实验结果表明, OR-DisCCA的性能比相关方法更优。
다시도학습방법통과시도간호보신식적융합,체도증강단일시도방법적로봉성병제승학습성능적목적。전형상관분석(canonical correlation analysis,간칭CCA)시일충중요적다시도신식융합기술。기연구적시침대동일조목표량조불동관측수거간적상관성,목표시득도일조상관성최대적투영향량。단당면대표호유서적분류임무시,CCA인몰유이용류신식화류간유서신식,조성료대분류성능적제약。위차,통과장유서류신식감입 CCA 진행확전,발전출유서판별전형상관분석(ordinal discriminative canonical correlation analysis,간칭 OR-DisCCA)。실험결과표명, OR-DisCCA적성능비상관방법경우。
Multi-View learning is a method to improve the robustness and learning performance of single-view learning by fusing the complementary information. Canonical correlation analysis (CCA) which is used to analyze correlation between two datasets of the same objects is an important method for multi-view feature fusion. CCA aims to seek a pair of projections associated with the two sets of data such that they are maximally correlated. However, CCA results in constraint of the classification performance due to not utilizing the class information or ordinal information of different classes for some applications in which the data labels are ordinal. In order to compensate such a shortcoming, ordinal discriminative canonical correlation analysis (OR-DisCCA) is proposed in this paper by incorporating the class information and ordinal information for extending the traditional CCA. The experimental results show that OR-DisCCA outperforms existing related methods.