Machine learning

Mathematical analysis

Supervised learning

Generalization

MNIST database

Engineering

Manifold alignment

Artificial intelligence

Deep learning

Pattern recognition (psychology)

Mathematics

Manifold (fluid mechanics)

Computer science

Nonlinear dimensionality reduction

Laplace operator

Regularization (linguistics)

Dimensionality reduction

Semi-supervised learning

Laplacian matrix

Artificial neural network

Mechanical engineering

IEEE Transactions on Circuits and Systems I Regular Papers

Broad Learning System (BLS) are widely used in many fields because of its strong feature extraction ability and high computational efficiency. However, the BLS is mainly used in supervised learning, which greatly limits the applicability of the BLS. And the obtained data is less labeled data, but is a large number of unlabeled data. Therefore, the BLS is extended based on the semi-supervised learning of manifold regularization framework to propose a semi-supervised broad learning system (SS-BLS). Firstly, the features are extracted from labeled and unlabeled data by building feature nodes and enhancement nodes. Then the manifold regularization framework is used to construct Laplacian matrix. Next, the feature nodes, enhancement nodes and Laplacian matrix are combined to construct the objective function, which is effectively solved by ridge regression in order to obtain the output coefficients. Finally, the validity of the SS-BLS is verified by three different complex data of G50C, MNIST, and NORB, respectively. The experiment result show that the SS-BLS can achieve higher classification accuracy for different complex data, takes on fast operation speed and strong generalization ability.

Semi-Supervised Broad Learning System Based on Manifold Regularization and Broad Network