Transductive Bayesian regression via manifold learning of prior data structure

Authors:
Hyejin Park;Heun A Kim;Seung-Ho Yang;Jaewook Lee
Affiliations:
Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), San 31 Hyoja, Pohang 790-784, South Korea;Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), San 31 Hyoja, Pohang 790-784, South Korea;Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), San 31 Hyoja, Pohang 790-784, South Korea;Department of Industrial Engineering, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul 151-744, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 13
Cited 3

Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
An Improved Cluster Labeling Method for Support Vector Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Letters: Linear local tangent space alignment and application to face recognition

Neurocomputing
Clustering Based on Gaussian Processes

Neural Computation
Prediction of pricing and hedging errors for equity linked warrants with Gaussian process models

Expert Systems with Applications: An International Journal
Constructing sparse kernel machines using attractors

IEEE Transactions on Neural Networks
Fast support-based clustering method for large-scale problems

Pattern Recognition
Dynamic Dissimilarity Measure for Support-Based Clustering

IEEE Transactions on Knowledge and Data Engineering
Predicting a distribution of implied volatilities for option pricing

Expert Systems with Applications: An International Journal
Calibrating parametric exponential Lévy models to option market data by incorporating statistical moments priors

Expert Systems with Applications: An International Journal
Dynamic pattern denoising method using multi-basin system with kernels

Pattern Recognition
Multi-basin particle swarm intelligence method for optimal calibration of parametric Lévy models

Expert Systems with Applications: An International Journal

Sequential manifold learning for efficient churn prediction

Expert Systems with Applications: An International Journal
Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction

Pattern Recognition
Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

During the last decades, many studies have been conducted on performing reliable prediction for high-dimensional data that are usually non-linearly correlated with complex patterns. In this paper, we propose a novel Bayesian regression method via non-linear dimensionality reduction. The method incorporates prior information on the underlying structure of original input features to preserve input-output patterns on reduced features, and to provide distributions of predicted values. To verify the effectiveness of the proposed method, we conducted simulations on benchmark and real-world data. Results showed that the method not only better predicts a distribution of forecast estimates compared with other methods, but also more robust and consistent performance on prediction.