Simultaneous localized feature selection and model detection for gaussian mixtures
Related Experiment Video
Updated: Jun 21, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
Published on: June 2, 2010
Yuanhong Li1, Ming Dong, Jing Hua
1Machine Vision and Pattern Recognition Laboratory, Department of Computer Science, Wayne State University, Detroit, MI 48202, USA. yhli@wayne.edu
Abstract:
In this paper, we propose a novel approach of simultaneous localized feature selection and model detection for unsupervised learning. In our approach, local feature saliency, together with other parameters of Gaussian mixtures, are estimated by Bayesian variational learning. Experiments performed on both synthetic and real-world data sets demonstrate that our approach is superior over both global feature selection and subspace clustering methods.
More Related Videos
12:27Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
07:35Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Related Concept Videos
Difference from Background: Limit of Detection
The LOD indicates the presence or absence...
Mass Spectrometry: Complex Analysis
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...