A linear discriminant analysis method based on mutual information maximization

We present a new linear discriminant analysis method based on information theory, where the mutual information between linearly transformed input data and the class labels is maximized. First, we introduce a kernel-based estimate of mutual information with a variable kernel size. Furthermore, we devise a learning algorithm that maximizes the mutual information w.r.t. the linear transformation. Two experiments are conducted: the first one uses a toy problem to visualize and compare the transformation vectors in the original input space; the second one evaluates the performance of the method for classification by employing cross-validation tests on four datasets from the UCI repository. Various classifiers are investigated. Our results show that this method can significantly boost class separability over conventional methods, especially for nonlinear classification.     

基于互信息的主成分分析特征选择算法

主成分分析是一种常用的特征选择算法,经典方法是计算各个特征之间的相关,但是相关无法评估变量间的非线性关系.互信息可用于衡量两个变量间相互依赖的强弱程度,且不局限于线性相关,鉴于此,提出一种基于互信息的主成分分析特征选择算法.该算法计算特征间的互信息,以互信息矩阵的特征值作为评价准则确定主成分的个数,并衡量主成分分析特征选择的效果.通过实例对所提出方法和传统主成分分析方法进行比较,并以神经网络为分类器分析分类效果.     

Multi-label feature selection based on neighborhood mutual information

Multi-label learning deals with data associated with a set of labels simultaneously. Like traditional single-label learning, the high-dimensionality of data is a stumbling block for multi-label learning. In this paper, we first introduce the margin of instance to granulate all instances under different labels, and three different concepts of neighborhood are defined based on different cognitive viewpoints. Based on this, we generalize neighborhood information entropy to fit multi-label learning and propose three new measures of neighborhood mutual information. It is shown that these new measures are a natural extension from single-label learning to multi-label learning. Then, we present an optimization objective function to evaluate the quality of the candidate features, which can be solved by approximating the multi-label neighborhood mutual information. Finally, extensive experiments conducted on publicly available data sets verify the effectiveness of the proposed algorithm by comparing it with state-of-the-art methods.     

Feature selection with dynamic mutual information

Feature selection plays an important role in data mining and pattern recognition, especially for large scale data. During past years, various metrics have been proposed to measure the relevance between different features. Since mutual information is nonlinear and can effectively represent the dependencies of features, it is one of widely used measurements in feature selection. Just owing to these, many promising feature selection algorithms based on mutual information with different parameters have been developed. In this paper, at first a general criterion function about mutual information in feature selector is introduced, which can bring most information measurements in previous algorithms together. In traditional selectors, mutual information is estimated on the whole sampling space. This, however, cannot exactly represent the relevance among features. To cope with this problem, the second purpose of this paper is to propose a new feature selection algorithm based on dynamic mutual information, which is only estimated on unlabeled instances. To verify the effectiveness of our method, several experiments are carried out on sixteen UCI datasets using four typical classifiers. The experimental results indicate that our algorithm achieved better results than other methods in most cases.     

基于最大联合条件互信息的特征选择

在高维数据如图像数据、基因数据、文本数据等的分析过程中,当样本存在冗余特征时会大大增加问题分析复杂难度,因此在数据分析前从中剔除冗余特征尤为重要。基于互信息（MI）的特征选择方法能够有效地降低数据维数,提高分析结果精度,但是,现有方法在特征选择过程中评判特征是否冗余的标准单一,无法合理排除冗余特征,最终影响分析结果。为此,提出一种基于最大联合条件互信息的特征选择方法（MCJMI）。MCJMI选择特征时考虑整体联合互信息与条件互信息两个因素,两个因素融合增强特征选择约束。在平均预测精度方面,MCJMI与信息增益（IG）、最小冗余度最大相关性（mRMR）特征选择相比提升了6个百分点;与联合互信息（JMI）、最大化联合互信息（JMIM）相比提升了2个百分点;与LW向前搜索方法（SFS-LW）相比提升了1个百分点。在稳定性方面,MCJMI稳定性达到了0.92,优于JMI、JMIM、SFS-LW方法。实验结果表明MCJMI能够有效地提高特征选择的准确率与稳定性。     

基于互信息的多级特征选择算法

针对在特征选择中选取特征较多时造成的去冗余过程很复杂的问题,以及一些特征需与其他特征组合后才会与标签有较强相关度的问题,提出了一种基于互信息的多级特征选择算法（MI_MLFS）。首先,根据特征与标签的相关度,将特征分为强相关、次强相关和其他特征;其次,选取强相关特征后,在次强相关特征中,选取冗余度较低的特征;最后,选取能增强已选特征集合与标签相关度的特征。在15组数据集上,将MI_MLFS与ReliefF、最大相关最小冗余（mRMR）算法、基于联合互信息（JMI）算法、条件互信息最大化准则（CMIM）算法和双输入对称关联（DISR）算法进行对比实验,结果表明MI_MLFS在支持向量机（SVM）和分类回归树（CART）分类器上分别有13组和11组数据集获得了最高的分类准确率。相较多种经典特征选择方法,MI_MLFS算法有更好的分类性能。     

优化的互信息特征选择方法

在文本分类中,互信息是一种被广泛应用的特征选择方法,但是该方法仅考虑了特征的文档频而没有考虑特征的词频,导致它经常倾向于选择出现频率较低的特征。为此,提出了一个新的文档频并把它引入到互信息方法中,从而获得了一种优化的互信息方法。该优化的互信息方法不但考虑了特征的文档频而且还考虑了特征出现的词频。实验结果表明该优化的互信息方法性能良好。     

基于潜在狄利克雷分配模型和互信息的无监督特征选取法

为解决互信息(MI)在特征选取中的类别缺失和倾向低频词问题,提出 LDA-σ方法。该方法使用潜在狄利克雷分配模型(LDA)提取潜在主题,以“词—主题”间互信息的标准差作为特征评估函数。在Reuters-21578语料集上提取特征词并进行分类,LDA-σ方法的微平均F1最高达0.9096;宏平均F1优于其他算法,最高达0.7823。实验表明,LDA-σ方法可用于文本特征选取。     

MIFS-ND: A mutual information-based feature selection method

Feature selection is used to choose a subset of relevant features for effective classification of data. In high dimensional data classification, the performance of a classifier often depends on the feature subset used for classification. In this paper, we introduce a greedy feature selection method using mutual information. This method combines both feature–feature mutual information and feature–class mutual information to find an optimal subset of features to minimize redundancy and to maximize relevance among features. The effectiveness of the selected feature subset is evaluated using multiple classifiers on multiple datasets. The performance of our method both in terms of classification accuracy and execution time performance, has been found significantly high for twelve real-life datasets of varied dimensionality and number of instances when compared with several competing feature selection techniques.     

基于专家特征的条件互信息多标记特征选择算法

特征选择对于分类器的分类精度和泛化性能起重要作用。目前的多标记特征选择算法主要利用最大相关性最小冗余性准则在全部特征集中进行特征选择,没有考虑专家特征,因此多标记特征选择算法的运行时间较长、复杂度较高。实际上,在现实生活中专家依据几个或者多个关键特征就能够直接决定整体的预测方向。如果提取关注这些信息,必将减少特征选择的计算时间,甚至提升分类器性能。基于此,提出一种基于专家特征的条件互信息多标记特征选择算法。首先将专家特征与剩余的特征相联合,再利用条件互信息得出一个与标记集合相关性由强到弱的特征序列,最后通过划分子空间去除冗余性较大的特征。该算法在7个多标记数据集上进行了实验对比,结果表明该算法较其他特征选择算法有一定优势,统计假设检验与稳定性分析进一步证明了所提出算法的有效性和合理性。     

基于互信息的文本特征选择方法研究与改进

通过对互信息（MI）文本特征选择方法与信息增益、卡方统计方法的实验研究比较,发现了影响MI方法性能的主要因素是特征选择过程中的随机性,通过加入扰动因子的方法对MI方法进行了改进,消除了随机性的影响,实验表明,改进后的MI方法与信息增益、卡方统计方法比较,具有较明显的优势。     

一种改进的基于条件互信息的特征选择算法

目前在文本分类领域较常用到的特征选择算法中,仅仅考虑了特征与类别之间的关联性,而对特征与特征之间的关联性没有予以足够的重视,这导致了特征之间预测能力的相互削弱,无法选出最有效的特征。提出了一种新的用于文本分类的特征选择算法（CMIM）,它可以帮助选出区分能力强、弱相关的特征。经实验验证,CMIM比传统的特征选择算法具有更好的性能。     

Spectral feature projections that maximize Shannon mutual information with class labels

Determining optimal subspace projections that can maintain task-relevant information in the data is an important problem in machine learning and pattern recognition. In this paper, we propose a nonparametric nonlinear subspace projection technique that maintains class separability maximally under the Shannon mutual information (MI) criterion. Employing kernel density estimates for nonparametric estimation of MI makes possible an interesting marriage of kernel density estimation-based information theoretic methods and kernel machines, which have the ability to determine nonparametric nonlinear solutions for difficult problems in machine learning. Significant computational savings are achieved by translating the definition of the desired projection into the kernel-induced feature space, which leads to obtain analytical solution.     

多标签学习中基于互信息的快速特征选择方法

针对传统的基于启发式搜索的多标记特征选择算法时间复杂度高的问题，提出一种简单快速的多标记特征选择（EF-MLFS）方法。首先使用互信息（MI）衡量每个维度的特征与每一维标记之间的相关性，然后将所得相关性相加并排序，最后按照总的相关性大小进行特征选择。将所提方法与六种现有的比较有代表性的多标记特征选择方法作对比，如最大依赖性最小冗余性（MDMR）算法和基于朴素贝叶斯的多标记特征选择（MLNB）方法等。实验结果表明，EF-MLFS方法进行特征选择并分类的结果在平均准确率、覆盖率、海明损失等常见的多标记分类评价指标上均达最优；该方法无需进行全局搜索，因此时间复杂度相较于MDMR、对偶多标记应用（PMU）等方法也有明显降低。     

Feature subset selection wrapper based on mutual information and rough sets

In this paper, we introduced a novel feature selection method based on the hybrid model (filter-wrapper). We developed a feature selection method using the mutual information criterion without requiring a user-defined parameter for the selection of the candidate feature set. Subsequently, to reduce the computational cost and avoid encountering to local maxima of wrapper search, a wrapper approach searches in the space of a superreduct which is selected from the candidate feature set. Finally, the wrapper approach determines to select a proper feature set which better suits the learning algorithm. The efficiency and effectiveness of our technique is demonstrated through extensive comparison with other representative methods. Our approach shows an excellent performance, not only high classification accuracy, but also with respect to the number of features selected.     

基于二次Renyi熵的正则化互信息特征选择方法

提出了一种基于二次Renyi's熵的正则化互信息特征选择方法,该方法能高效地对互信息进行估计从而使计算复杂度大大降低。同时把正则化互信息特征选择方法与嵌入式方法相结合得到一个两段式特征选择算法,该算法可以找出更具特征的特征子集。通过实验比较了该方法与其他基于互信息的特征选择算法的效率与分类精度,结果表明该方法能够有效改善计算复杂度。     

基于互信息的多级特征选择算法

针对在特征选择中选取特征较多时造成的去冗余过程很复杂的问题，以及一些特征需与其他特征组合后才会与标签有较强相关度的问题，提出了一种基于互信息的多级特征选择算法（MI_MLFS）。首先，根据特征与标签的相关度，将特征分为强相关、次强相关和其他特征；其次，选取强相关特征后，在次强相关特征中，选取冗余度较低的特征；最后，选取能增强已选特征集合与标签相关度的特征。在15组数据集上，将MI_MLFS与ReliefF、最大相关最小冗余（mRMR）算法、基于联合互信息（JMI）算法、条件互信息最大化准则（CMIM）算法和双输入对称关联（DISR）算法进行对比实验，结果表明MI_MLFS在支持向量机（SVM）和分类回归树（CART）分类器上分别有13组和11组数据集获得了最高的分类准确率。相较多种经典特征选择方法，MI_MLFS算法有更好的分类性能。     

一种可最优化计算特征规模的互信息特征提取

利用矩阵特征向量分解，提出一种可最优化计算特征规模的互信息特征提取方法．首先，论述了高斯分布假设下的该互信息判据的类可分特性，并证明了现有典型算法都是本算法的特例；然后，在给出该互信息判据严格的数学意义基础上，提出了基于矩阵特征向量分解计算最优化特征规模算法；最后，通过实际数据验证了该方法的有效性     

An adaptive GOP structure selection for haar-like MCTF encoding based on mutual information

In conventional motion compensated temporal filtering based wavelet coding scheme, where the group of picture structure and low-pass frame position are fixed, variations in motion activities of video sequences are not considered. In this paper, we propose an adaptive group of picture structure selection scheme, which the group of picture size and low-pass frame position are selected based on mutual information. Furthermore, the temporal decomposition process is determined adaptively according to the selected group of picture structure. A large amount of experimental work is carried out to compare the compression performance of proposed method with the conventional motion compensated temporal filtering encoding scheme and adaptive group of picture structure in standard scalable video coding model. The proposed low-pass frame selection can improve the compression quality by about 0.3–0.5 dB comparing to the conventional scheme in video sequences with high motion activities. In the scenes with un-even variation of motion activities, e.g. frequent shot cuts, the proposed adaptive group of picture size can achieve a better compression capability than conventional scheme. When comparing to adaptive group of picture in standard scalable video coding model, the proposed group of picture structure scheme can lead to about 0.2~0.8 dB improvements in sequences with high motion activities or shot cut.

一种基于互信息的改进文本特征选择

提出了一种优化互信息文本特征选择方法。针对互信息模型的不足之处主要从三方面进行改进:用权重因子对正、负相关特征加以区分;以修正因子的方式在MI中引入词频信息对低频词进行抑制;针对特征项在文本里的位置差异进行基于位置的特征加权。该方法改善了MI模型的特征选择效率。文本分类实验结果验证了提出的优化互信息特征选择方法的合理性与有效性。