一种用于Web文本聚类的特征选择方法

特征选择已经广泛地应用在文本分类和文本聚类中,相对于无监督的特征选择方法,有监督的特征选择方法在过滤噪音等方面更为有效.但是,由于缺少类标签,它很难应用到文本聚类中.提出了一种针对Web文本聚类的新的特征选择算法--基于k-means的多特征联合选择算法(MFCC).MFCC充分利用了一个特征空间的中间聚类结果来帮助另一个特征空间进行特征选择.实验证明,MFCC有效地提高了聚类质量.     

一种高效的用于文本聚类的无监督特征选择算法

特征选择虽然非常成功地应用于文本分类,但却很少用于文本聚类,这是因为那些高效的特征选择方法通常都是有监督的特征选择算法,它们因为需要类信息而无法直接应用于文本聚类．为了能将这些方法应用到文本聚类上,提出了一种新的无监督特征选择算法：基于K—Means的特征选择算法(KFS)．这个算法通过在不同K—Means聚类结果上使用有监督特征选择的方法,成功地选择出了最为重要的一小部分特征,使文本聚类的性能提高了近15％．     

一种新的鲁棒声纹特征提取与融合方法

为提高说话人确认系统在噪声环境下的鲁棒性,在利用听觉外周模型改进Mel频率倒谱系数(Mel FrequencyCepstral Coefficient,MFCC)的基础上,结合感知线性预测系数(Perceptual Linear Predictive Coefficient,PLPC),以类间区分度为依据,在特征域对两种声纹特征进行融合,提出一种新的声纹特征提取方法,并对基于该特征的说话人确认系统的噪声鲁棒性进行研究。针对不同信噪比的语音信号进行了融合特征与原始特征的对比实验,结果表明,融合特征在模拟餐厅噪声环境中的错误率更低,较MFCC与PLPC分别降低了2.2%和3.1%,说话人确认系统在噪声中的鲁棒性得到提升。     

基于类信息的文本聚类中特征选择算法

文本聚类属于无监督的学习方法,由于缺乏类信息还很难直接应用有监督的特征选择方法,因此提出了一种基于类信息的特征选择算法,此算法在密度聚类算法的聚类结果上使用信息增益特征选择法重新选择最有分类能力的特征,实验验证了算法的可行性和有效性。     

Text Clustering with Feature Selection by Using Statistical Data

Feature selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the corpus. In this paper, we propose a new supervised feature selection method, named CHIR, which is based on the chi² statistic and new statistical data that can measure the positive term-category dependency. We also propose a new text clustering algorithm, named text clustering with feature selection (TCFS). TCFS can incorporate CHIR to identify relevant features (i.e., terms) iteratively, and the clustering becomes a learning process. We compared TCFS and the K-means clustering algorithm in combination with different feature selection methods for various real data sets. Our experimental results show that TCFS with CHIR has better clustering accuracy in terms of the F-measure and the purity.     

Simultaneous feature selection and clustering using mixture models

Clustering is a common unsupervised learning technique used to discover group structure in a set of data. While there exist many algorithms for clustering, the important issue of feature selection, that is, what attributes of the data should be used by the clustering algorithms, is rarely touched upon. Feature selection for clustering is difficult because, unlike in supervised learning, there are no class labels for the data and, thus, no obvious criteria to guide the search. Another important problem in clustering is the determination of the number of clusters, which clearly impacts and is influenced by the feature selection issue. In this paper, we propose the concept of feature saliency and introduce an expectation-maximization (EM) algorithm to estimate it, in the context of mixture-based clustering. Due to the introduction of a minimum message length model selection criterion, the saliency of irrelevant features is driven toward zero, which corresponds to performing feature selection. The criterion and algorithm are then extended to simultaneously estimate the feature saliencies and the number of clusters.     

A clustering based feature selection method in spectro-temporal domain for speech recognition

Spectro-temporal representation of speech has become one of the leading signal representation approaches in speech recognition systems in recent years. This representation suffers from high dimensionality of the features space which makes this domain unsuitable for practical speech recognition systems. In this paper, a new clustering based method is proposed for secondary feature selection/extraction in the spectro-temporal domain. In the proposed representation, Gaussian mixture models (GMM) and weighted K-means (WKM) clustering techniques are applied to spectro-temporal domain to reduce the dimensions of the features space. The elements of centroid vectors and covariance matrices of clusters are considered as attributes of the secondary feature vector of each frame. To evaluate the efficiency of the proposed approach, the tests were conducted for new feature vectors on classification of phonemes in main categories of phonemes in TIMIT database. It was shown that by employing the proposed secondary feature vector, a significant improvement was revealed in classification rate of different sets of phonemes comparing with MFCC features. The average achieved improvements in classification rates of voiced plosives comparing to MFCC features is 5.9% using WKM clustering and 6.4% using GMM clustering. The greatest improvement is about 7.4% which is obtained by using WKM clustering in classification of front vowels comparing to MFCC features.     

A NEW SUPERVISED FEATURE SELECTION METHOD FOR PATTERN CLASSIFICATION

With the rapid development of information techniques, the dimensionality of data in many application domains, such as text categorization and bioinformatics, is getting higher and higher. The high‐dimensionality data may bring many adverse situations, such as overfitting, poor performance, and low efficiency, to traditional learning algorithms in pattern classification. Feature selection aims at reducing the dimensionality of data and providing discriminative features for pattern learning algorithms. Due to its effectiveness, feature selection is now gaining increasing attentions from a variety of disciplines and currently many efforts have been attempted in this field. In this paper, we propose a new supervised feature selection method to pick important features by using information criteria. Unlike other selection methods, the main characteristic of our method is that it not only takes both maximal relevance to the class labels and minimal redundancy to the selected features into account, but also works like feature clustering in an agglomerative way. To measure the relevance and redundancy of feature exactly, two different information criteria, i.e., mutual information and coefficient of relevance, have been adopted in our method. The performance evaluations on 12 benchmark data sets show that the proposed method can achieve better performance than other popular feature selection methods in most cases.     

基于子带信息的鲁棒语音特征提取框架

本文提出一种鲁棒语音特征提取框架。通过使用一种基于子带能量分布的噪声估计方法,无需静音段,就可以估计出带噪语音的子带噪声,同时提出结合谱减和谱加权方法对特征进行处理,最终生成具有较高鲁棒性的特征。 实验证明,在语音识别系统中,这种特征可以有效提高语音识别的鲁棒性,在噪声较强(信噪比0dB到15dB)的情况下,识别率可以提高20%以上;并且,在干净语音的情况下又能保证识别率没有大的下降;同时,这种特征上的处理方法对各种噪声的适应能力都很强,无需对噪声进行预先分类即可得到很好的抗噪效果。     

改进的HMM和小波神经网络的抗噪语音识别

通过MFFC计算出的语音特征系数,由于语音信号的动态性,帧之间有重叠,噪声的影响,使特征系数不能完全反映出语音的信息。提出一种隐马尔可夫模型（HMM）和小波神经网络（WNN）混合模型的抗噪语音识别方法。该方法对MFCC特征系数利用小波神经网络进行训练,得到新的MFCC特征系数。实验结果表明,在噪声环境下,该混合模型比单纯HMM具有更强的噪声鲁棒性,明显改善了语音识别系统的性能。     

说话人识别中基于Fisher比的特征组合方法

为了提高说话人识别的准确率,可以同时采用多个特征参数,针对综合特征参数中各维分量对识别结果的影响可能不一样,同等对待并不一定是最优的方案这个问题,提出基于Fisher准则的梅尔频率倒谱系数(MFCC)、线性预测梅尔倒谱系数(LPMFCC)、Teager能量算子倒谱参数(TEOCC)相混合的特征参数提取方法。首先,提取语音信号的MFCC、LPMFCC和TEOCC三种参数;然后,计算MFCC和LPMFCC参数中各维分量的Fisher比,分别选出六个Fisher比高的分量与TEOCC参数组合成混合特征参数;最后,采用TIMIT语音库和NOISEX-92噪声库进行说话人识别实验。仿真实验表明,所提方法与MFCC、LPMFCC、MFCC+LPMFCC、基于Fisher比的梅尔倒谱系数混合特征提取方法以及基于主成分分析(PCA)的特征抽取方法相比,在采用高斯混合模型(GMM)和BP神经网络的平均识别率在纯净语音环境下分别提高了21.65个百分点、18.39个百分点、15.61个百分点、15.01个百分点与22.70个百分点;在30 dB噪声环境下,则分别提升了15.15个百分点、10.81个百分点、8.69个百分点、7.64个百分点与17.76个百分点。实验结果表明,该混合特征参数能够有效提高说话人识别率,且具有更好的鲁棒性。     

基于HMM模型的语音单元边界的自动切分

基于隐尔马可夫模型（HMM）的强制对齐方法被用于文语转换系统（TTS）语音单元边界切分.为提高切分准确性,本文对HMM模型的特征选择,模型参数和模型聚类进行优化.实验表明：12维静态Mel频率倒谱系数（MFCC）是最优的语音特征;HMM模型中的状态模型采用单高斯;对于特定说话人的HMM模型,使用分类与衰退树（CART）聚类生成的绑定状态模型个数在3 000左右最优.在英文语音库中音素边界切分的实验中,切分准确率从模型优化前的77.3%提高到85.4%.     

Unsupervised feature selection via joint local learning and group sparse regression

Feature selection has attracted a great deal of interest over the past decades. By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improved. Because label information is expensive to obtain, unsupervised feature selection methods are more widely used than the supervised ones. The key to unsupervised feature selection is to find features that effectively reflect the underlying data distribution. However, due to the inevitable redundancies and noise in a dataset, the intrinsic data distribution is not best revealed when using all features. To address this issue, we propose a novel unsupervised feature selection algorithm via joint local learning and group sparse regression (JLLGSR). JLLGSR incorporates local learning based clustering with group sparsity regularized regression in a single formulation, and seeks features that respect both the manifold structure and group sparse structure in the data space. An iterative optimization method is developed in which the weights finally converge on the important features and the selected features are able to improve the clustering results. Experiments on multiple real-world datasets (images, voices, and web pages) demonstrate the effectiveness of JLLGSR.

     

复杂噪声中基于MFCC距离的语音端点检测算法

为提高复杂噪声环境下语音信号端点检测的准确率,提出一种基于梅尔频谱倒谱系数(MFCC)距离的多维特征语音信号端点检测算法。通过计算语音信号的MFCC距离,结合短时能量和短时过零率对特征距离进行修正,并更新其阈值,建立自适应噪声模型,实现复杂噪声中语音信号端点的准确检测。实验结果表明,与基于双门限能量和基于倒谱距离的2种经典检测算法相比,在计算效率相同的条件下,该算法的检测准确率更高。     

Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery

Features extracted from real world applications increase dramatically, while machine learning methods decrease their performance given the previous scenario, and feature reduction is required. Particularly, for fault diagnosis in rotating machinery, the number of extracted features are sizable in order to collect all the available information from several monitored signals. Several approaches lead to data reduction using supervised or unsupervised strategies, where the supervised ones are the most reliable and its main disadvantage is the beforehand knowledge of the fault condition. This work proposes a new unsupervised algorithm for feature selection based on attribute clustering and rough set theory. Rough set theory is used to compute similarities between features through the relative dependency. The clustering approach combines classification based on distance with clustering based on prototype to group similar features, without requiring the number of clusters as an input. Additionally, the algorithm has an evolving property that allows the dynamic adjustment of the cluster structure during the clustering process, even when a new set of attributes feeds the algorithm. That gives to the algorithm an incremental learning property, avoiding a retraining process. These properties define the main contribution and significance of the proposed algorithm. Two fault diagnosis problems of fault severity classification in gears and bearings are studied to test the algorithm. Classification results show that the proposed algorithm is able to select adequate features as accurate as other feature selection and reduction approaches.     

A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection

Models based on data mining and machine learning techniques have been developed to detect the disease early or assist in clinical breast cancer diagnoses. Feature selection is commonly applied to improve the performance of models. There are numerous studies on feature selection in the literature, and most of the studies focus on feature selection in supervised learning. When class labels are absent, feature selection methods in unsupervised learning are required. However, there are few studies on these methods in the literature. Our paper aims to present a hybrid intelligence model that uses the cluster analysis techniques with feature selection for analyzing clinical breast cancer diagnoses. Our model provides an option of selecting a subset of salient features for performing clustering and comprehensively considers the use of most existing models that use all the features to perform clustering. In particular, we study the methods by selecting salient features to identify clusters using a comparison of coincident quantitative measurements. When applied to benchmark breast cancer datasets, experimental results indicate that our method outperforms several benchmark filter- and wrapper-based methods in selecting features used to discover natural clusters, maximizing the between-cluster scatter and minimizing the within-cluster scatter toward a satisfactory clustering quality.     

鲁棒的特征权重自调节软子空间聚类算法

针对已有的特征权重自调节软子空间(SC-FWSA)聚类算法存在对噪声敏感的问题,基于一种非欧氏距离,提出一种鲁棒的特征权重自调节软子空间(RSC-FWSA)聚类算法。RSC-FWSA在迭代过程中自适应地为数据生成一个权函数,通过计算每一类数据的加权平均来计算聚类中心,这种"加权平均"使得聚类中心的估计对噪声相对不敏感,从而可以提升算法对带噪声数据和复杂结构数据的聚类精度。人工数据和真实数据上的对比性实验,验证了RSC-FWSA算法的有效性。特别是人工带噪声数据和3个真实数据:Wine, Zoo以及Breastcancer上的实验结果表明,RSC-FWSA可以显著提升原对应算法的聚类精度。RSC-FWSA具有的强鲁棒性使得该算法适用于高维带噪声和复杂结构数据的聚类问题。     

基于优化DBSCAN聚类算法的晶圆图预处理

晶圆图是由半导体生产过程中对晶圆进行可测试性检测而得到的,通过对晶圆图进行分类可以为生产过程中出现的问题提供依据,从而解决问题,降低生产成本.在对晶圆图进行分类之前,最重要的是特征提取,晶圆图除了本身拥有一定的空间图案以外,还存在着很多的噪声,影响着特征提取的过程.传统的DBSCAN算法用于滤波,需要人为确定两个参数,最小邻域Eps和最小点数MinPts,参数的选择直接影响了聚类的准确性.为此,提出一种基于优化DBSCAN聚类算法的滤波方式,自动确定DBSCAN的参数,以解决传统的手动设定参数的弊端.该算法基于参数自动寻优策略,选取DBSCAN 聚类后簇内密度参数和簇间密度参数的综合指标来评定最优参数.实验结果表明,该算法能自动并合理地选择较好的参数,具有很好的聚类效果,对后续的特征提取及分类也具有很大的帮助.     

基于K-均值聚类的无监督的特征选择方法

模式识别方法首先要解决的一个问题就是特征选择,目前许多方法考虑了有监督学习的特征选择问题,对无监督学习的特征选择问题却涉及得很少。依据特征对分类结果的影响和特征之间相关性分析两个方面提出了一种基于K-均值聚类方法的特征选择算法,用于无监督学习的特征选择问题。     

基于语谱图的江西境内赣方言自动分区研究

汉语方言分区研究是语言学的重要组成部分。鉴于传统基于词汇和语法的人工方言分区方法具有一定的主观性,该文研究了如何有效利用语音本身特征进行方言的自动分区。论文首先构建了江西省11个省辖市、91个下辖县级行政区的时长约1 500分钟的1 223条语音语料库,然后在传统的MFCC语音特征提取基础上,提出了基于CNN的自编码降维语谱图的深度学习特征提取模型,对降维后的语音特征分别采用k均值算法聚类、高斯混合聚类和层次聚类对方言自动分区。实验结果表明,新型语谱图特征的聚类性能度量内部指标DBI指数以及DI指数显著优于传统MFCC特征,维度为16时语谱图和MFCC下的拼接特征聚类效果与传统人工方言分区较为接近。