基于监督学习的稀疏编码及在数据表示中的应用  

Sparse coding based supervised leaning and its application to data representation

作　　者：舒振球[1] 赵春霞[1] 张浩峰[1] 

机构地区：[1]南京理工大学计算机科学与工程学院,南京210094

出　　处：《控制与决策》2014年第6期1115-1119,共5页Control and Decision

基　　金：国家自然科学基金项目(61272220;61101197);江苏省自然基金青年项目(BK2012399);江苏省普通高校研究生创新计划项目(CXLX13 19);哈尔滨工程大学水下机器人技术国家科技重点实验室开放基金项目

摘　　要：针对稀疏编码在数据表示时没有利用样本类别信息的问题,提出一种基于监督学习的稀疏编码算法,并应用于数据表示.首先利用样本的类别信息构建图,直接提取样本的鉴别结构信息;然后利用基向量拟合鉴别结构特性向量,进而在基向量中嵌入样本的鉴别信息;最后对样本逐个进行稀疏表示.在COIL20和PIE图像库的实验结果表明,相比几种无监督矩阵分解算法,所提出的算法更利于样本的表示和分类.For the problem of the sparse coding methods not making full use of the label information in data representation, an algorithm, the supervised learning sparse coding, is proposed which can be applied to data representation. Firstly, the proposed algorithm can build the graph via the label information. Thus it directly extracts the discriminate information of the data and then tries to learn the basis which can best fit the discriminate vector. Therefore, it can find a basis set embedding the discriminant information of the samples which are individually for sparse representation. The experiments on the COIL20 and PIE image data sets demonstrate that the proposed algorithm can provide a better representation and classification than the traditional unsupervised matrix factorization algorithms.

关 键 词：矩阵分解 鉴别分析 稀疏编码 数据表示 拟合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]