CAJ | 학술논문

本文主要关注多视图数据的分类问题。考虑到集成分类方法可组合多个弱分类器构成一个强分类器，以及主题模型能学习复杂数据的语义表示，本文试图将集成学习思想引入主题模型中，以便同时学习多视图数据的分类规则和预测性语义特征。具体地，结合概率主题模型LDA模型和集成分类方法Softmax混合模型，提出了一个多视图有监督的分类模型。基于变分EM方法，推导了该模型的参数估计算法。两个真实图像数据集上的实验结果表明了提出模型有较好的分类性能。
본문주요관주다시도수거적분류문제。고필도집성분류방법가조합다개약분류기구성일개강분류기，이급주제모형능학습복잡수거적어의표시，본문시도장집성학습사상인입주제모형중，이편동시학습다시도수거적분류규칙화예측성어의특정。구체지，결합개솔주제모형LDA모형화집성분류방법Softmax혼합모형，제출료일개다시도유감독적분류모형。기우변분EM방법，추도료해모형적삼수고계산법。량개진실도상수거집상적실험결과표명료제출모형유교호적분류성능。
In the paper ,we mainly focus on classifition on multi-view data .Considering that ensemble methods can combine weak classifiers to construct a strong classifier ,and topic model can learn latent representations from complex data ,we try to intro-duce ensemble idea to topic model ,such that predictive latent representation could be obtained and multi-view classifier could be learned .We propose multi-view supervised latent Dirichlet allocation (multi-view sLDA ) model by combining latent Dirichlet allo-cation model and the mixture of softmax model which is an ensemble classification model .Moreover ,we derive a parameter estima-tion algorithm of the proposed model based on variational expectation maximization (EM ) procedure .The experimental results on two real datasets show the effectiveness of the proposed model .