计算和存储空间受限下的数据稀疏核分析方法

Computation and Store Space Constrained-Based Sparse Kernel Data Analysis

针对核主成分分析算法广泛面临的训练样本数量大而带来的计算和存储空间的问题,提出了基于1类支持向量理论的稀疏核主成分分析算法,该方法适合于计算和存储空间受限下的应用场合,如小型硬件平台下的图像检索系统、医学辅助诊断系统等.通过求解最优方程找到能够代表原始样本空间的少量典型样本,这些样本作为计算核数据矩阵,大大节省了核矩阵计算的时间和存储空间成本,在有限的训练样本集上最大限度在硬件平台下图像处理领域有效提高识别率和计算效率.

In order to solve the computation and storage space problems of kernel principal component analysis, which come from the large number of the training samples, this paper presents one-class support vector based sparse kernel princi- pal component analysis （SKPCA）. This method can be used in the computation-constrained and space-constrained applica- tions, for example, a small scale hardware platform based image retrieval system, medical assistant diagnosis system, and so on. The method uses the constrained optimization equation to seek the few representative samples, and the few representative samples are used to compute the kernel matrix. The method decreases the computing time and decreases the storage space. So under conditions of the limited training samples, the method is to improve the performance of accuracy and efficiency for hardware computing platform-based image processing.