基于伪PCA的手写数字识别算法

摘要/Abstract

摘要： PCA(Principal Component Analysis)是最重要的数据降维算法之一,针对降维过程出现的信息丢失问题,学术界说法不一。基于此,文中提出了一种新的改进算法(Similar Principal Component Analysis,SPCA),新算法在处理过程中保留了部分细节信息。以手写数字(MNIST)数据库为例,将原始向量组进行临近特征筛选,得出多维复合非正交特征向量组;将训练库所得的向量组与测试集的向量组进行比对,识别出所测试的手写数字。结果表明,该算法能够以较少量的训练样本实现对测试样本的较为完全的识别。

关键词: 非正交特征, 临近特征, 手写数字识别, 主成分分析

Abstract: Principal component analysis (PCA) is one of the most important data reduction algorithms,there is much-maligned views in the process of handling data.A novel improved similar principal component analysis (SPCA) algorithm which is based on principal component analysis (PCA) algorithm was proposed in this paper.This algorithm can keep some detail information in the process.Taking the MNIST handwritten numeral database as an example, the near feature vector is chosen in original vectors to get the groups of non-orthogonal feature vectors.Then,the vectors of trai-ning library is compared with the vectors of testing library,and the recognition rate is calculated.Recognition results indicate that the algorithm can make high identification of the testing samples through a small number of training samples.

Key words: Handwritten numeral recognition, Near feature vector, Non-orthogonal feature vectors, Principal component analysis(PCA)

中图分类号:

TP391.41

韩旭, 刘强, 许瑾, 谌海云. 基于伪PCA的手写数字识别算法[J]. 计算机科学, 2018, 45(11A): 278-281. https://doi.org/

HAN Xu, LIU Qiang, XU Jin, CHEN Hai-yun. Handwritten Numeral Recognition Algorithm Based on Similar Principal Component Analysis[J]. Computer Science, 2018, 45(11A): 278-281. https://doi.org/

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