Institute of Chemical Engineering, Vienna University of Technology, A-1060 Vienna, Austria.
Anal Chim Acta. 2011 Oct 31;705(1-2):48-55. doi: 10.1016/j.aca.2011.03.031. Epub 2011 Mar 24.
Random projection (RP) is a simple and fast linear method for dimensionality reduction of high-dimensional multivariate data, independent from the data. The method is briefly described and a new memory-saving algorithm is presented for the generation of random projection vectors. Application of RP to data from scanning experiments with a time-of-flight secondary ion mass spectrometer (TOF-SIMS) showed that data reduced by RP have a satisfying discriminant property for separating target material and minerals without using any knowledge about the composition of the sample. A selection method--based on low dimensional RP data--is described and successfully tested for automatic recognition of characteristic, diverse locations of a sample surface. RP is demonstrated as an unbiased, powerful method, especially for large data sets, severe hardware restrictions (such as in space experiments) or the need for fast data evaluation of hyperspectral data.
随机投影（RP）是一种简单快速的线性方法，用于降低高维多元数据的维度，与数据无关。本文简要描述了该方法，并提出了一种新的节省内存的算法，用于生成随机投影向量。将 RP 应用于飞行时间二次离子质谱仪（TOF-SIMS）扫描实验数据表明，通过 RP 降低的数据对于分离目标材料和矿物具有令人满意的判别特性，而无需使用有关样品组成的任何知识。本文描述了一种基于低维 RP 数据的选择方法，并成功地对样品表面的特征、多样位置进行了自动识别测试。RP 被证明是一种无偏、强大的方法，特别是对于大数据集、硬件限制苛刻（例如在空间实验中）或需要快速评估高光谱数据的情况。
