三向阵列数据的快速近似karhunen - lo变换

Hayato Itoh, A. Imiya, T. Sakai
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引用次数: 1

摘要

在生物医学图像分析中处理的器官、细胞和细胞中的微结构是体积数据。从面向对象数据分析的角度来看,我们需要将这些数据作为体数据来处理和分析,而不是嵌入到高维向量空间中。体积数据的采样值表示为三向阵列数据。因此,多向数据的主成分分析是基于子空间的模式识别、数据检索和体积数据压缩的关键技术。对于单向阵列(矢量形式)问题,离散余弦变换矩阵是主成分分析中特征矩阵的一种很好的松弛解。利用主成分分析的代数性质,导出了三向数据阵列主成分分析的近似快速算法。
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Fast Approximate Karhunen-Loève Transform for Three-Way Array Data
Organs, cells and microstructures in cells dealt with in biomedical image analysis are volumetric data. We are required to process and analyse these data as volumetric data without embedding into higher-dimensional vector space from the viewpoints of object oriented data analysis. Sampled values of volumetric data are expressed as three-way array data. Therefore, principal component analysis of multi-way data is an essential technique for subspace-based pattern recognition, data retrievals and data compression of volumetric data. For one-way array (the vector form) problem the discrete cosine transform matrix is a good relaxed solution of the eigenmatrix for principal component analysis. This algebraic property of principal component analysis, derives an approximate fast algorithm for PCA of three-way data arrays.
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