Manifold regularized multiple kernel learning with Hellinger distance

Manifold regularized multiple kernel learning with Hellinger distance

Článek WoS

The aim of this paper is to solve the problem of unsupervised manifold regularization being used under supervised classification circumstance. This paper not only considers that the manifold information of data can provide useful information but also proposes a supervised method to compute the Laplacian graph by using the label information and the Hellinger distance for a comprehensive evaluation of the similarity of data samples. Meanwhile, multi-source or complex data is increasing nowadays. It is desirable to learn from several kernels that are adaptable and flexible to deal with this type of data. Therefore, our classifier is based on multiple kernel learning, and the proposed approach to supervised classification is a multiple kernel model with manifold regularization to incorporate intrinsic geometrical information. Finally, a classifier that minimizes the testing error and considers the geometrical structure of data is put forward. The results of experiments with other methods show the effectiveness of the proposed model and computing the inner potential geometrical information is useful for classification.

The aim of this paper is to solve the problem of unsupervised manifold regularization being used under supervised classification circumstance. This paper not only considers that the manifold information of data can provide useful information but also proposes a supervised method to compute the Laplacian graph by using the label information and the Hellinger distance for a comprehensive evaluation of the similarity of data samples. Meanwhile, multi-source or complex data is increasing nowadays. It is desirable to learn from several kernels that are adaptable and flexible to deal with this type of data. Therefore, our classifier is based on multiple kernel learning, and the proposed approach to supervised classification is a multiple kernel model with manifold regularization to incorporate intrinsic geometrical information. Finally, a classifier that minimizes the testing error and considers the geometrical structure of data is put forward. The results of experiments with other methods show the effectiveness of the proposed model and computing the inner potential geometrical information is useful for classification.

Multiple kernel learning; Manifold regularization; Hellinger distance

Multiple kernel learning; Manifold regularization; Hellinger distance

YANG, T.; FU, D.; LI, X.; ŘÍHA, K.

2021

01.11.2019

SPRINGER

NEW YORK

1386-7857

Cluster Computing-The Journal of Networks Software Tools and Applications

22

6

Spojené státy americké

13843

13851

9

https://link.springer.com/article/10.1007/s10586-018-2106-2