期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2018
卷号：170
期号：4
页码：042051
DOI：10.1088/1755-1315/170/4/042051
语种：English
出版社：IOP Publishing
摘要：Locally Linear Embedding (LLE) algorithm is one of promising NonLinear Dimensionality Reduction (NLDR) method for feature extraction. Like most NLDR algorithms, LLE operates in a batch or off-line mode, in other words, for newly coming samples, the old data augmented by the new samples must be completely recalculated by LLE algorithm, which is computationally intensive. Back propagation neural network (BP) is a nonlinear mapping method, and it can learn all the information of a dataset, further, when BP is trained well, it is effective to predict new data. Hence, in this paper, BP is combined with LLE (BPLLE) to deal with out-of-sample data. Four synthetic datasets and two real datasets are given to demonstrate that BPLLE is more valid than several classical incremental LLE algorithms.
