Intrinsic Bayesian model for high-dimensional unsupervised reduction

Authors:
Longcun Jin;Wanggen Wan;Yongliang Wu;Bin Cui;Xiaoqing Yu
Affiliations:
School of Software Engineering, South China University of Technology, Wushan Rd 381, Guangzhou 510006, PR China and School of Communication and Information Engineering, Shanghai University, Yancha ...;School of Communication and Information Engineering, Shanghai University, Yanchang Rd 149, Shanghai 200072, PR China;School of Communication and Information Engineering, Shanghai University, Yanchang Rd 149, Shanghai 200072, PR China;School of Communication and Information Engineering, Shanghai University, Yanchang Rd 149, Shanghai 200072, PR China;School of Software Engineering, South China University of Technology, Wushan Rd 381, Guangzhou 510006, PR China
Venue:
Neurocomputing
Year:
2012

Citing 27
Cited 0

Bias reduction in skewed binary classification with Bayesian neural networks

Neural Networks
Generalized Dimension-Reduction Framework for Recent-Biased Time Series Analysis

IEEE Transactions on Knowledge and Data Engineering
Incremental Nonlinear Dimensionality Reduction by Manifold Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-preserving hybrid data reduction based on fuzzy-rough techniques

Pattern Recognition Letters
Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis

IEEE Transactions on Knowledge and Data Engineering
Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameterization

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Subset Selection and Ranking for Data Dimensionality Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
High-dimensional delay selection for regression models with mutual information and distance-to-diagonal criteria

Neurocomputing
On the performance of bias-reduction techniques for variance estimation in approximate Bayesian bootstrap imputation

Computational Statistics & Data Analysis
From outliers to prototypes: Ordering data

Neurocomputing
Denoising using local projective subspace methods

Neurocomputing
Reduction of Average Path Length in Binary Decision Diagrams by Spectral Methods

IEEE Transactions on Computers
Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
A new multimedia information data mining method

Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Lanczos Vectors versus Singular Vectors for Effective Dimension Reduction

IEEE Transactions on Knowledge and Data Engineering
Constrained Dimensionality Reduction Using a Mixed-Norm Penalty Function with Neural Networks

IEEE Transactions on Knowledge and Data Engineering
High-dimensional labeled data analysis with topology representing graphs

Neurocomputing
Support vector machine for functional data classification

Neurocomputing
Classification of chirp signals using hierarchical Bayesianlearning and MCMC methods

IEEE Transactions on Signal Processing
Semi-Supervised Linear Spectral Unmixing Using a Hierarchical Bayesian Model for Hyperspectral Imagery

IEEE Transactions on Signal Processing - Part I
Sensor Planning for Mobile Robot Localization---A Hierarchical Approach Using a Bayesian Network and a Particle Filter

IEEE Transactions on Robotics
Bayesian classification and feature reduction using uniform Dirichlet priors

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervised nonlinear dimensionality reduction for visualization and classification

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mixed-Initiative Human–Robot Interaction Using Hierarchical Bayesian Networks

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Unsupervised multistage image classification using hierarchical clustering with a bayesian similarity measure

IEEE Transactions on Image Processing
A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields

IEEE Transactions on Image Processing
Bayesian Approach With Hidden Markov Modeling and Mean Field Approximation for Hyperspectral Data Analysis

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper proposes a novel algorithm for high-dimensional unsupervised reduction from intrinsic Bayesian model. The proposed algorithm is to assume that the pixel reflectance results from nonlinear combinations of pure component spectra contaminated by additive noise. The constraints are naturally expressed in intrinsic Bayesian literature by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. The proposed algorithm consists of intrinsic Bayesian inductive cognition part and hierarchical reduction algorithm model part. The algorithm has several advantages over traditional distance based on Bayesian reduction algorithms. The proposed reduction algorithm from intrinsic Bayesian inductive cognitive model is used to decide which dimensions are advantageous and to output the recommended dimensions of the hyperspectral image. The algorithm can be interpreted as a novel fast reduction inference method for intrinsic Bayesian inductive cognitive model. We describe procedures for learning the model hyperparameters, computing the dimensions distribution, and extensions to the intrinsic Bayesian inductive cognition model. Experimental results on hyperspectral data demonstrate robust and useful properties of the proposed reduction algorithm.