Shared Autoencoder Gaussian Process Latent Variable Model for Visual Classification

Multiview learning reveals the latent correlation among different modalities and utilizes the complementary information to achieve a better performance in many applications. In this paper, we propose a novel multiview learning model based on the Gaussian process latent variable model (GPLVM) to lear...

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Vydané v:IEEE transaction on neural networks and learning systems Ročník 29; číslo 9; s. 4272 - 4286
Hlavní autori: Li, Jinxing, Zhang, Bob, Zhang, David
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.09.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Autoencoder
Classification
Correlation
Data models
Decoding
discriminative
Economic models
Gaussian process
Gaussian process (GP)
Gaussian processes
Information processing
Kernel
latent variable model
Manifolds
Mapping
Monte Carlo simulation
multiview
Optimization
Parameter estimation
Regularization
Semantics
Visual discrimination learning
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:Multiview learning reveals the latent correlation among different modalities and utilizes the complementary information to achieve a better performance in many applications. In this paper, we propose a novel multiview learning model based on the Gaussian process latent variable model (GPLVM) to learn a set of nonlinear and nonparametric mapping functions and obtain a shared latent variable in the manifold space. Different from the previous work on the GPLVM, the proposed shared autoencoder Gaussian process (SAGP) latent variable model assumes that there is an additional mapping from the observed data to the shared manifold space. Due to the introduction of the autoencoder framework, both nonlinear projections from and to the observation are considered simultaneously. Additionally, instead of fully connecting used in the conventional autoencoder, the SAGP achieves the mappings utilizing the GP, which remarkably reduces the number of estimated parameters and avoids the phenomenon of overfitting. To make the proposed method adaptive for classification, a discriminative regularization is embedded into the proposed method. In the optimization process, an efficient algorithm based on the alternating direction method and gradient decent techniques is designed to solve the encoder and decoder parts alternatively. Experimental results on three real-world data sets substantiate the effectiveness and superiority of the proposed approach as compared with the state of the art.
Bibliografia:ObjectType-Article-1
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2017.2761401