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Unsupervised Subspace Learning With Flexible Neighboring
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PRE
AI
DOI：10.1109/TNNLS.2021.3105813.png)
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摘要
En 中文
Graph-based subspace learning has been widely used in various applications as the rapid growth of data dimension, while the graph is constructed by affinity matrix of input data. However, it is difficult for these subspace learning methods to preserve the intrinsic local structure of data with the high-dimensional noise. To address this problem, we proposed a novel unsupervised dimensionality reduction approach named unsupervised subspace learning with flexible neighboring (USFN). We learn a similarity graph by adaptive probabilistic neighborhood learning process to preserve the manifold structure of high-dimensional data. In addition, we utilize the flexible neighboring to learn projection and latent representation of manifold structure of high-dimensional data to remove the impact of noise. The adaptive similarity graph and latent representation are jointly learned by integrating adaptive probabilistic neighborhood learning and manifold residue term into a unified objection function. The experimental results on synthetic and real-world datasets demonstrate the performance of the proposed unsupervised subspace learning USFN method.
Keyword:
Dimensionality reduction
Manifolds
Principal component analysis
Covariance matrices
Task analysis
Probabilistic logic
Laplace equations
Flexible neighboring
similarity graph
subspace learning
unsupervised learning
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
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