Randolph Timothy W
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
Cancer Biomark. 2006;2(3-4):135-44. doi: 10.3233/cbm-2006-23-405.
Classification of data that arise as signals or images often requires a standardization step so that information extracted from biologically equivalent signals can be quantified for comparison across classes. Differences in global trend, total energy, high-frequency noise and/or local background can arise from variabilities due to instrumentation or conditions during data collection. This article considers some common ways in which such variation is adjusted for and introduces a generalization of the popular "standard normal variate" transformation. Based on a wavelet decomposition this generalization provides increased flexibility for normalizing spectral data affected by local background noise. Examples from three types of spectroscopy data illustrate the method and its properties.
对作为信号或图像出现的数据进行分类通常需要一个标准化步骤，以便可以对从生物学等效信号中提取的信息进行量化，从而在不同类别之间进行比较。由于数据采集过程中的仪器或条件差异，可能会出现全局趋势、总能量、高频噪声和/或局部背景的差异。本文考虑了一些针对此类变化进行调整的常见方法，并引入了流行的“标准正态变量”变换的一种推广。基于小波分解，这种推广为归一化受局部背景噪声影响的光谱数据提供了更大的灵活性。来自三种光谱数据类型的示例说明了该方法及其特性。
