[1]DENG Tingquan,WANG Qiang.Semi-supervised class preserving locally linear embedding[J].CAAI Transactions on Intelligent Systems,2021,16(1):98-107.[doi:10.11992/tis.202003007]
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Semi-supervised class preserving locally linear embedding

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 1 Page number: 98-107 Column: 学术论文—知识工程 Public date: 2021-01-05
Title:
Semi-supervised class preserving locally linear embedding
Author(s):
DENG Tingquan;  WANG Qiang
College of Mathematical Sciences, Harbin engineering university, Harbin 150001, China
Keywords:
nonlinear feature extraction; manifold learning; semi-supervised; labeled information; clustering; visualization
CLC:
TP181
DOI:
10.11992/tis.202003007
Abstract:
To make local linear embedding (LLE), the nonlinear feature extraction method for unsupervised high-dimensional data, more optimal in classification or clustering learning, we propose a nonlinear semi-supervised class preserving local linear embedding (SSCLLE) feature extraction method. This method integrates semi-supervised information into LLE. First, pseudo-labels are assigned to the nearby neighbors of the labeled samples to increase the number of labeled samples. Second, the distance between the labeled samples is partially adjusted to reduce the distance between similar samples and expand the distance between heterogeneous samples. Simultaneously, the constraints of the globally same sample spacing and heterogeneous sample spacing are added in the local linear embedding optimization objective function so that the extracted low-dimensional features can ensure that the same sample points are near each other, whereas the heterogeneous sample points are separated from each other. In a series of experiments, the clustering accuracy and visualization effect of the proposed method are significantly higher than those of unsupervised LLE and the existing semi-supervised flow feature extraction methods, indicating that the features extracted by this method have good class retention characteristics.
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