有限制的MFA-ICA的算法及其在图像特征提取中的应用

统的独立成分分析(IndependentComponentAnalysis,ICA)是一种无噪声模型,而实际应用中噪声是存在的。根据多元统计中的因子分析模型,改变其假设条件,从而得到一种有噪声ICA模型,对于模型参数,引入平均场近似(MeanFieldApproximation,MFA)原理来求解。针对图像特征提取,通过增加对模型参数的一些限制,使其能得到更为独立的图像特征,为图像识别提供更可靠的特征信息,从而大大提高识别率。通过仿真模拟图形以及ORL人脸数据进行实验,将传统的独立成分分析算法、无限制的MFA ICA算法以及增加限制条件的MFA ICA算法进行比较,从仿真模拟图形实验结果看,限制的MFA ICA算法能分离出更独立的特征,同时利用限制的MFA ICA算法识别效果明显优于传统ICA算法和无限制MFA ICA算法。

Restrictive MFC-ICA algorithm and its application in extracting image feature

A traditional Independent Component Analysis(ICA) is a noise-free model and has some rigorous conditions. However, there are many noises. In this paper, these conditions were changed by the factor analysis in multi-variable statistics and a noisy ICA model was formed. The theory of Mean Field Approximation (MFA) in statistical physics was used to estimate the model parameters. In order to obtain more independent features in feature extraction, some restrictions were added to model parameters, such as non-negative mixing matrices and non-negative source signals. Experiments were done by two different cases that simulated graphics and ORL face databases. The simulated graphics experimental results show this proposed method can extract more independent features than the traditional ICA and the unrestricted MFAICA method do.