iNet: visual analysis of irregular transition in multivariate dynamic networks

Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along time. They are common in multimedia applications. Anomaly detection is one of the essential tasks in analyzing these networks though it is not well addressed. In this paper, we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic networks. We conclude features of rare categories and two types of anomalies of rare categories. Then we present a novel rare category detection method, called DIRAD, to detect rare category candidates with anomalies. We develop a prototype system called iNet, which integrates two major visualization components, including a glyph-based rare category identifier, which helps users to identify rare categories among detected substructures, a major view, which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex attributes. Evaluations, including an algorithm performance evaluation, a case study, and a user study, are conducted to test the effectiveness of proposed methods.     

Ensemble learning for independent component analysis

It is well known that the applicability of independent component analysis (ICA) to high-dimensional pattern recognition tasks such as face recognition often suffers from two problems. One is the small sample size problem. The other is the choice of basis functions (or independent components). Both problems make ICA classifier unstable and biased. In this paper, we propose an enhanced ICA algorithm by ensemble learning approach, named as random independent subspace (RIS), to deal with the two problems. Firstly, we use the random resampling technique to generate some low dimensional feature subspaces, and one classifier is constructed in each feature subspace. Then these classifiers are combined into an ensemble classifier using a final decision rule. Extensive experimentations performed on the FERET database suggest that the proposed method can improve the performance of ICA classifier.     

基于改进的独立分量分析的人脸识别方法

将独立分量分析（Independent Component Analysis，ICA）作为人脸特征提取方法。ICA所提取的特征分类能力强、相互独立，对像素间高阶统计特性敏感，并且不易受光照变化的影响。实验结果表明，基于IcA的人脸特征提取方法的识别性能优于特征脸法。针对传统的ICA算法（Informax算法）存在迭代次数多，难收敛，并且需要人工设定步长来调整学习速度的不足，本文采用FastICA作为ICA的快速算法，并将其关键迭代步骤加以改进，减少了耗时的雅可比矩阵求逆的运算次数。所提出的改进的FastICA具有无需人工参与，收敛速度快，迭代次数少的优点。在特征选择方面，本文将遗传算法（Genetie Algorithm，GA）应用到独立分量的选择与优化中，从而在保证较高识别性能的前提下，获得最优的人脸特征子集。     

一种改进的基于ICA特征子空间的目标识别方法

介绍了独立分量分析（ICA）的基本原理和算法,并提出了基于独立分量分析的特征子空间的目标识别方法。该方法首先利用快速独立分量分析（FastICA）算法对训练集目标图像进行ICA分解,据此建立特征子空间,然后根据待识别图像在特征子空间的投影系数进行判别。本文的改进在于根据类内类间距离比值最小化准则进行最有利于分类的特征的优化选择。实验结果显示,和传统方法相比,改进的方法能有效提高识别的准确率和效率。     

基于独立分子量分析的图象分离技术及应用

简要介绍了有关独立分量分析的基本理论和算法,探讨了独立分量分析在序列图象处理方面的应用,提出了基于独立分量分析的运动目标检测新方法,同时用独立分量分析方法对含有运动目标的序列图象进行了独立分量分离的试验,试验中,首先获取序列图象的独立分量和模型混合矩阵,然后将含有背景干扰的独立分量置零,并用混合矩阵进行逆运算,从而获得非常清晰的运动目标轨迹,试验结果表明,这种独立分量分析方法具有良好的盲源分离性能,而且在运动目标检测等方面,基于独立分量分析的检测方法较传统的检测方法更有效。     

基于ICA与HMM的表情识别

独立分量分析(independent component analysis,ICA)是一种盲源分离的有效方法,为了进一步有效提取表情图像中隐藏的信息和提高表情识别率,可将它应用于人脸表情识别。由于脸部表情为人类情感、认知过程的研究提供了极为重要的测量依据,因此表情特征的提取和特征序列所代表的表情状态是表情识别过程中的重要步骤。为了更好地进行表情和情感的分类,提出了一种ICA结合隐马尔可夫模型(HMM)识别表情的情感分类系统,该系统首先利用ICA算法进行表情特征提取,为了加快特征提取的速度,这里采用了FastICA算法;然后通过7个训练好的HMM进行表情识别。实验结果显示,该系统使人脸表情识别的整体效果有了提高,取得了令人满意的效果,可以用来识别人脸表情。     

ICA在思维脑电特征提取中的应用

简要介绍了独立分量分析（ICA）的基本思想及算法，并将其应用在基于多导思维脑电（mental EEG)的特征提取方面。实验结果表明：ICA可以将脑电信号中包含的心电（ECG）、眼电（EOG）等多种干扰信号成功地分离出来，较好地完成了脑电消噪预处理工作。同时，通过使用ICA方法对不同心理作业的脑电信号进行分析处理，发现了与心理作业相对应的脑电独立分量特征，这些稳定的独立分量特征为心理作业分类和脑一机接口技术提供了新的实现方法。     

Independent component analysis of Gabor features for face recognition

We present an independent Gabor features (IGFs) method and its application to face recognition. The novelty of the IGF method comes from 1) the derivation of independent Gabor features in the feature extraction stage and 2) the development of an IGF features-based probabilistic reasoning model (PRM) classification method in the pattern recognition stage. In particular, the IGF method first derives a Gabor feature vector from a set of downsampled Gabor wavelet representations of face images, then reduces the dimensionality of the vector by means of principal component analysis, and finally defines the independent Gabor features based on the independent component analysis (ICA). The independence property of these Gabor features facilitates the application of the PRM method for classification. The rationale behind integrating the Gabor wavelets and the ICA is twofold. On the one hand, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can, thus, produce salient local features that are most suitable for face recognition. On the other hand, ICA would further reduce redundancy and represent independent features explicitly. These independent features are most useful for subsequent pattern discrimination and associative recall. Experiments on face recognition using the FacE REcognition Technology (FERET) and the ORL datasets, where the images vary in illumination, expression, pose, and scale, show the feasibility of the IGF method. In particular, the IGF method achieves 98.5% correct face recognition accuracy when using 180 features for the FERET dataset, and 100% accuracy for the ORL dataset using 88 features.     

基于主分量特征与独立分量特征的人脸识别实验

PCA是一种基于二阶统计的最小均方误差意义上的最优维数压缩技术,PCA方法所抽取特征的各分量之间是统计不相关的。ICA方法使用数据的二阶和高阶信息抽取数据的独立分量特征。在人脸图象识别的实际应用中,PCA与ICA方法各有胜负。PCA方法抽取出的主分量特征与ICA方法抽取出的独立分量特征是对原数据的两类不同描述,并设计出一个基于这两类特征的分类器组合方案;联合使用这两类特征,实验得出的人脸识别结果显示,基于分类器组合方案的识别结果优于单独使用PCA特征或ICA特征的单分类器方法。     

人耳人脸特征融合在身份鉴别中的研究

针对单一人耳识别对姿态变化鲁棒性较差的问题,鉴于人脸在图像性质和生理位置上与人耳具有相似性和互补性,使用了多模态特征融合的方法提高姿态变化下的识别率.与传统的独立成分分析首先获得独立的基向量(ICAl)不同,提出了利用ICA直接获得独立的鉴剐特征的方法(ICA2).在USTB图像库上分别将两种ICA特征进行单模态和多模态的融合.实验表明,两种特征的融合提高了单一模态的识别率,并且多模态识别优于单一的人耳或人脸识别.     

Fast and robust fixed-point algorithms for independent componentanalysis

Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. We use a combination of two different approaches for linear ICA: Comon's information theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions     

Face representation using independent component analysis

This paper addresses the problem of face recognition using independent component analysis (ICA). More specifically, we are going to address two issues on face representation using ICA. First, as the independent components (ICs) are independent but not orthogonal, images outside a training set cannot be projected into these basis functions directly. In this paper, we propose a least-squares solution method using Householder Transformation to find a new representation. Second, we demonstrate that not all ICs are useful for recognition. Along this direction, we design and develop an IC selection algorithm to find a subset of ICs for recognition. Three public available databases, namely, MIT AI Laboratory, Yale University and Olivette Research Laboratory, are selected to evaluate the performance and the results are encouraging.     

Automatic recognition of handwritten characters via feature extraction and multi-level decision

Automatic recognition of handwritten alphanumeric characters is designed by making use of topological feature extraction and multi-level decision making. By properly specifying a set of easily detectable topological features, it is possible to convert automatically the handwritten characters into stylized forms and to classify them into primary categories. Each category contains one or several character pattern classes with similar topological configurations. Final recognition is accomplished by a secondary stage which performs local analysis on the characters in each primary category. The recognition system consists of two stages, global recognition followed by local recognition. Automatic character stylization results in pattern clustering which simplifies the classification tasks considerably, while allowing a high degree of generality in the acceptable writing format. Simulation of this scheme on a digital computer has shown only 2% misrecognition.This work was supported in part by the Office of Naval Research and the National Science Foundation.     

基于独立成分分析的表面缺陷特征提取与识别方法

为了提取表面缺陷图像特征,常对图像进行线性变换,但通常的wavelet变换、Gabor变换及其基函数都是预先定义和不变的,不能适应于缺陷图像的特点.为此提出基于独立成分分析(ICA)和拓扑独立成分分析(TICA)的特征提取方法,并将其应用于冷轧带钢表面缺陷自动识别.首先利用ICA和TICA从缺陷集中自适应地估计出基函数和滤波器,这些基适应于缺陷图像的特点;然后用与基对应的滤波器对缺陷图像滤波,提取滤波响应作为特征向量;最后用支持向量机对样本进行分类识别.该方法建立在对缺陷集无监督学习的基础上,能够自适应地提取缺陷图像的显著特征,且计算简单,可并行处理.实验结果表明,文中方法对形状类缺陷、纹理类缺陷及其他缺陷的识别率都非常高,总体识别率可达95.52％.     

一种基于分而治之的语音识别错误纠正方案*

介绍了一种基于分而治之的语音识别错误纠正方案,首先利用混淆网络把连续语音识别问题转换为顺序的、独立的分类子任务。每个分类子任务可看做是孤立词识别问题,通过训练专门的支持向量机来区分混淆网络的识别候选。提出了一种快速的基于码本转换的语音向量对齐方法,解决了变长语音向量无法直接作为支持向量机输入的问题。通过一个普通话音节识别任务的实验结果表明,该方案能有效提高系统的正确率。     

Independent component analysis using Potts models

We explore the extending application of Potts encoding to the task of independent component analysis, which primarily deals with the problem of minimizing the Kullback-Leibler divergence between the joint distribution and the product of all marginal distributions of output components. The competitive mechanism of Potts neurons is used to encode the overlapping projections from observations to output components. Based on these projections, the marginal distributions and the entropy of output components are made tractable for computation and the adaptation of the de-mixing matrix toward independent output components is obtained. The Potts model for ICA is well formulated by an objective function subject to a set of constraints, which leads to a novel energy function. A hybrid of the mean field annealing and the gradient descent method is applied to the energy function. Our approach to independent component analysis presents a new criterion for ICA. The performance of the Potts model for ICA given by our numerical simulations is encouraging.     

独立分量分析在高光谱图像舰船检测中的应用

根据海七舰船目标的特点,提出了一种基于独立分量分析的目标检测算法,应用于高光谱图像海上舰船检测.首先采用基于峰度的快速独立分量分析方法(FastICA)对高光谱图像进行处理,获得统计独立的独立分量影像,然后以偏度为特征度量指标从上述独立分量中选择特征影像,得到舰船目标的检测结果.应用于海上高光谱舰船图像,能够抑制背景中的海浪杂波及舰船尾迹对目标的影响,取得较好的检测效果.实验结果也进一步验证了基于峰度的FastICA算法在高光谱数据分析中的有效性.     

基于PSO与ICA的表情特征提取

提出基于粒子群优化（PSO）与独立分量分析（ICA）的表情特征提取方法。首先利用ICA算法对表情图像数据建立基本的独立基向量求解框架；为了减少计算复杂度，然后利用PSO算法对处理后的表情图像数据搜索最优的解集合；最后利用支持向量机（SVM）作为算法验证的分类器。实验结果表明该算法在保证较高表情识别率的基础上加快了表情图像特征提取的速度。     

能量熵与峰值窗口结合去除眼电伪迹研究

为改进传统独立分量分析自动去除眼电伪迹算法中存在识别眼电分量速度慢、需采集同步参考眼电信号、丢失脑电信号问题,提出一种不需要参考眼电信号的眼电伪迹自动识别去除方法。利用FastICA分解出独立分量,计算各独立分量频谱能量熵,以频谱能量熵值作为判据识别出眼电分量;然后使用峰值窗口分离出眼电分量中存在的脑电信号,与其他独立分量进行拼接;利用FastICA逆变换重构出去眼电伪迹的脑电信号。实验结果表明：该方法能准确快速自动地去除眼电伪迹,并较好地保留其他的脑电信号成分;频谱能量熵识别眼电伪迹平均用时为0.01?s,准确率为98％,适用于实时EOG去除。     

基于分块独立分量分析的人脸识别

提出了一种基于分块独立分量分析(BICA)的特征提取方法。该方法通过将人脸分块降低了光照条件、人脸表情等外在因素对人脸识别的影响,并先后将分块后重组的矩阵的行和列作为训练样本提取独立分量,由于训练样本维数很小,因此它降低了传统独立分量分析（ICA）方法中存在的高维小样本问题产生的识别错误率,同时减少了识别时间。在Yale人脸库和AR人脸库上验证了该算法的有效性。