Semi-supervised Gaussian process latent variable model with pairwise constraints

Authors:
Xiumei Wang;Xinbo Gao;Yuan Yuan;Dacheng Tao;Jie Li
Affiliations:
School of Sciences, Xidian University, Xi'an 710071, China and School of Electronic Engineering, Xidian University, Xi'an 710071, China;School of Electronic Engineering, Xidian University, Xi'an 710071, China;School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, United Kingdom;School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N4, 639798, Singapore;School of Electronic Engineering, Xidian University, Xi'an 710071, China
Venue:
Neurocomputing
Year:
2010

Citing 20
Cited 3

Latent variable models and factors analysis

Latent variable models and factors analysis
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Bayesian Classification With Gaussian Processes

IEEE Transactions on Pattern Analysis and Machine Intelligence
A probabilistic framework for semi-supervised clustering

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning a Mahalanobis Metric from Equivalence Constraints

The Journal of Machine Learning Research
Gaussian Processes for Classification: Mean-Field Algorithms

Neural Computation
Local distance preservation in the GP-LVM through back constraints

ICML '06 Proceedings of the 23rd international conference on Machine learning
Supervised probabilistic principal component analysis

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Distance Metrics with Contextual Constraints for Image Retrieval

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Variational Bayesian multinomial probit regression with Gaussian process priors

Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)

Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

The Journal of Machine Learning Research
Robust self-tuning semi-supervised learning

Neurocomputing
Constraint Score: A new filter method for feature selection with pairwise constraints

Pattern Recognition
Locality sensitive semi-supervised feature selection

Neurocomputing
Pairwise constraint propagation by semidefinite programming for semi-supervised classification

Proceedings of the 25th international conference on Machine learning
Iterative subspace analysis based on feature line distance

IEEE Transactions on Image Processing
Riemannian manifold learning for nonlinear dimensionality reduction

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Binary Two-Dimensional PCA

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Gabor-Based Region Covariance Matrices for Face Recognition

IEEE Transactions on Circuits and Systems for Video Technology

Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction

Pattern Recognition
Dimensionality reduction with adaptive graph

Frontiers of Computer Science: Selected Publications from Chinese Universities
Fuzzy deep belief networks for semi-supervised sentiment classification

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In machine learning, Gaussian process latent variable model (GP-LVM) has been extensively applied in the field of unsupervised dimensionality reduction. When some supervised information, e.g., pairwise constraints or labels of the data, is available, the traditional GP-LVM cannot directly utilize such supervised information to improve the performance of dimensionality reduction. In this case, it is necessary to modify the traditional GP-LVM to make it capable of handing the supervised or semi-supervised learning tasks. For this purpose, we propose a new semi-supervised GP-LVM framework under the pairwise constraints. Through transferring the pairwise constraints in the observed space to the latent space, the constrained priori information on the latent variables can be obtained. Under this constrained priori, the latent variables are optimized by the maximum a posteriori (MAP) algorithm. The effectiveness of the proposed algorithm is demonstrated with experiments on a variety of data sets.