Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data
Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data
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DOI：
10.1111/1541-0420.00017
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发表时间：
2003-03-01
期刊：
影响因子：
1.9
通讯作者：
Feng, ZD
中科院分区：
文献类型：
--
作者：
Qu, YS;Adam, BL;Feng, ZD
当变量数远大于观测数时，本文提出了一种利用小波变换进行判别分析的数据约简方法。该方法以前列腺癌研究为例进行说明，其中样本量为248，变量数量为48，538（使用蛋白质芯片技术生成）。使用离散小波变换，48，538个数据点由1271个小波系数表示。信息标准确定了1271小波系数的11个最高的歧视权力。具有11个小波系数的线性分类器在单独的测试集中检测到前列腺癌，灵敏度为97%，特异性为100%。
We present a method of data reduction using a wavelet transform in discriminant analysis when the number of variables is much greater than the number of observations. The method is illustrated with a prostate cancer study, where the sample size is 248, and the number of variables is 48,538 (generated using the ProteinChip technology). Using a discrete wavelet transform, the 48,538 data points are represented by 1271 wavelet coefficients. Information criteria identified 11 of the 1271 wavelet coefficients with the highest discriminatory power. The linear classifier with the 11 wavelet coefficients detected prostate cancer in a separate test set with a sensitivity of 97% and specificity of 100%.