Abstract:
A study is made on feature extraction and a new feature extraction method is suggested. The method overcomes the shortcomings of the feature extraction method which was proposed by Roman W recently, which has no discriminant information. Face feature is obtained by the combination of PCA and LDA, the dimensionality is further reduced by using attribute reduction algorithm in the theory of rough set. Experimental results indicate that the proposed method has good performance.
Key words:
attributes reduction,
attribute significance,
error of reduct approximation
摘要: 对特征抽取方法进行了研究,提出一种新的特征抽取方法,克服了Roman W等提出的特征抽取方法中缺乏鉴别信息的缺点。通过对高维的人脸数据用PCA和LDA降维,利用粗糙集理论中的属性约简算法进行进一步的维数压缩。实验结果表明,该方法具有良好的性能。
关键词:
属性约简,
属性重要度,
近似约简误差
CLC Number:
SHAO Jun; WU Xiao-jun; WANG Shi-tong; YANG Jing-yu. 基于代数特征与粗糙集相结合的特征提取方法[J]. Computer Engineering, 2008, 34(3): 225-227.
邵 俊;吴小俊;王士同;杨静宇. 基于代数特征与粗糙集相结合的特征提取方法[J]. 计算机工程, 2008, 34(3): 225-227.