Semisupervised Feature Selection via Structured Manifold Learning

doi:10.1109/TCYB.2021.3052847

Semisupervised Feature Selection via Structured Manifold Learning

2022-07-01

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DOI：10.1109/TCYB.2021.3052847

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Recently, semisupervised feature selection has gained more attention in many real applications due to the high cost of obtaining labeled data. However, existing methods cannot solve the ``multimodality'' problem that samples in some classes lie in several separate clusters. To solve the multimodality problem, this article proposes a new feature selection method for semisupervised task, namely, semisupervised structured manifold learning (SSML). The new method learns a new structured graph which consists of more clusters than the known classes. Meanwhile, we propose to exploit the submanifold in both labeled data and unlabeled data by consuming the nearest neighbors of each object in both labeled and unlabeled objects. An iterative optimization algorithm is proposed to solve the new model. A series of experiments was conducted on both synthetic and real-world datasets and the experimental results verify the ability of the new method to solve the multimodality problem and its superior performance compared with the state-of-the-art methods.

Keywords:

Feature extraction

Laplace equations

Training

Manifolds

Manifold learning

Training data

Task analysis

Feature selection

local structure learning

manifold learning

semisupervised feature selection

structure learning

Semisupervised Feature Selection via Structured Manifold Learning

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