高斯判别模型对人类心脏疾病的预测分析

随着现代化生活方式普及,大部分劳动者利用个人电脑进行办公,长时间坐着办公减少了必要的身体锻炼容易引发心脏疾病。本文从个人身体信息出发,利用分类预测模型建立对个人心脏疾病的预警机制。对于个体的年龄、性别、胸痛类型、静息血压等分类数值以及胆固醇含量、静息血压、最大心率等连续型数值进行描述性统计分析。区别逻辑回归等判别式学习算法,另辟蹊径。从贝叶斯先验角度出发引入了生成模型中推导严谨的高斯判别模型。

基于BIC和G_PLDA的说话人分离技术研究

传统的以贝叶斯信息准则(Bayesian information criterion,BIC)作为相似性度量的说话人分离技术,在短时对话的分离任务中能取得较好的效果,但是随着对话时长的增加,BIC的单高斯模型不足以描述不同说话人数据的分布,且层次聚类(Hierarchical agglomerative clustering,HAC)时,区分相同说话人和不同说话人的门限值难以划定.针对此问题,提出基于短时BIC和长时G_PLDA的融合方法,充分利用BIC在短时聚类的可靠性和G_PLDA在长时段上的优异区分性,在美国国家标准技术局(NIST)08Summed测试集上的实验表明,该方法将分类错误率(DER)从BIC基线系统的2.34%降到1.54%,性能相对提升34.2%.

Comparison of wrist motion classification methods using surface electromyogram

The Gaussian mixture model (GMM), k-nearest neighbor (k-NN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non-normalization in GMM, k-NN, and LDA.

A novel multimode process monitoring method integrating LCGMM with modified LFDA

Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.

Ridge-Forward Quadratic Discriminant Analysis in High-Dimensional Situations

Quadratic discriminant analysis is a classical and popular classification tool,but it fails to work in high-dimensional situations where the dimension p is larger than the sample size n.To address this issue,the authors propose a ridge-forward quadratic discriminant(RFQD) analysis method via screening relevant predictors in a successive manner to reduce misclassification rate.The authors use extended Bayesian information criterion to determine the final model and prove that RFQD is selection consistent.Monte Carlo simulations are conducted to examine its performance.