Gaussian Processes Autoencoder for Dimensionality Reduction

Xinwei Jiang, Junbin Gao, Xia Hong, Zhihua Cai

    Research output: Book chapter/Published conference paperConference paperpeer-review

    12 Citations (Scopus)


    Learning low dimensional manifold from highly nonlinear data of high dimensionality has become increasingly important for discovering intrinsic representation that can be utilized for data visualization and preprocessing. The autoencoder is a powerful dimensionality reduction technique based on minimizing reconstruction error, and it has regained popularity because it has been efficiently used for greedy pre-training of deep neural networks. Compared to Neural Network (NN), the superiority of Gaussian Process (GP) has been shown in model inference, optimization and performance. GP has been successfully applied in nonlinear Dimensionality Reduction (DR) algorithms, such as Gaussian Process Latent Variable Model (GPLVM). In this paper we propose the Gaussian Processes Autoencoder Model (GPAM) for dimensionality reduction by extending the classic NN based autoencoder to GP based autoencoder. More interestingly, the novel model can also be viewed as back constrained GPLVM (BC-GPLVM) where the back constraint smooth function is represented by a GP. Experiments verify the performance of the newly proposed model.
    Original languageEnglish
    Title of host publicationPAKDD 2014
    EditorsV.S. Tseng
    Place of PublicationCham Heidelberg
    Number of pages12
    ISBN (Print)9783319066042
    Publication statusPublished - 2014
    EventPacific-Asia Conference on Knowledge Discovery and Data Mining - Tainan, Taiwan, China
    Duration: 13 May 201416 May 2014


    ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining


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