Spiegelberg Jakob, Rusz Ján, Thersleff Thomas, Pelckmans Kristiaan
Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden.
Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden.
Ultramicroscopy. 2017 Mar;174:14-26. doi: 10.1016/j.ultramic.2016.12.014. Epub 2016 Dec 16.
A set of geometric data decomposition methods is discussed. In particular, randomized vertex component analysis (RVCA), an extension of vertex component analysis (VCA) for the application to noisy data, is established. Minimum volume simplex analysis (MVSA), a recent technique for the extraction of endmembers in the absence of pure pixels, is presented. A comparison between MVSA and the previously presented technique of Bayesian Linear Unmixing (BLU) is drawn. Lastly, the efficiency of these methods for high-dimensional data is examined. Improvement on the extracted source components spectral signatures are achieved by establishing Gaussian mixture modeling as extraction technique.
讨论了一组几何数据分解方法。特别地，建立了随机顶点成分分析（RVCA），它是顶点成分分析（VCA）在噪声数据应用中的扩展。提出了最小体积单纯形分析（MVSA），这是一种在没有纯像素情况下提取端元的最新技术。对MVSA和先前提出的贝叶斯线性分解（BLU）技术进行了比较。最后，检验了这些方法对高维数据的效率。通过建立高斯混合模型作为提取技术，提高了提取的源成分光谱特征。
