关键词: 半监督学习, 成对约束, 自编码器, 稀疏编码

Abstract: The models of non-parametric kernel learning methods are generally built based on the manifold assumption and pairwise constraints. But it is so expensive for some intricate high-dimensional and sparse data, due to the high complexity of non-parametric kernel learning methods. In this paper, a sparse autoencoder with non-parametric method based on sparse self-coding is proposed. Through adding sparse autoencoder, the method not only overcomes overfitting problem and improves robustness, but also runs faster. The kernel cluster experiments are conducted with the kernel obtained by the proposed method. All results demonstrate that the proposed method outperforms the traditional non-parametric kernel learning method.

Key words: semi-supervised learning, pairwise constraints, autoencoder, sparse coding