Title :
A noise-insensitive LLE algorithm
Author :
Yong, Zhou ; Yun, Gou Hong
Author_Institution :
Sch. of Comput. Sci. & Technol., China Univ. of Min. & Technol., Xuzhou, China
Abstract :
The core idea of LLE is that high-dimensional data can be seen near-linear dependency from the local perspective. That is, at a local scale, a data point can be linear representation by its nearest neighbor interpolation, but for noisy data, its local linear representation will produce deviations, as result, the error of dimension reduction is greater. To address the problem, this article determines system noise points according to the density of data set. The low density points judged to be noise points and the noise points are not used in process of dimension reduction. This method reduces the impact of noise on LLE effectively, which is proved by simulation experiment on Swissrol dataset.
Keywords :
interpolation; noise; nonlinear control systems; data set density; dimension reduction error; local linear representation; nearest neighbor interpolation; noise insensitive LLE algorithm; noisy data point; nonlinear control systems; Computer science; Data mining; Educational institutions; Informatics; Interpolation; Noise; Noise measurement; LLE; density; noise points;
Conference_Titel :
Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8041-8
DOI :
10.1109/COGINF.2010.5599812