Content driven dimensionality reduction at block level in the design of an efficient classifier for spatial multi spectral images

In case of spatial multi spectral images, such as remotely sensed earth cover, there could be many classes in one entire frame covering a large spatial stretch, because of which meaningful dimensionality reduction cannot perhaps be realizable without trading off with the quality of classification. However most often one would encounter in such images, presence of only a few classes in a small neighborhood, which would enable to devise a very effective dimensionality reduction around that small neighborhood identified as a block. Based on this theme a new method for dimensionality reduction is proposed in this paper.

The method proposed divides the image into uniform non-overlapping windows/blocks. The few features that are essential in discriminating classes in a window are identified. Clustering is performed independently on each of the blocks with the reduced set of features. These clusters in the blocks are later merged to obtain an overall classification of the entire image. The efficacy of the method is corroborated experimentally.     

Multistage approach for clustering and classification of ECG data

Accurate and fast approaches for automatic ECG data classification are vital for clinical diagnosis of heart disease. To this end, we propose a novel multistage algorithm that combines various procedures for dimensionality reduction, consensus clustering of randomized samples and fast supervised classification algorithms for processing of the highly dimensional large ECG datasets. We carried out extensive experiments to study the effectiveness of the proposed multistage clustering and classification scheme using precision, recall and F-measure metrics. We evaluated the performance of numerous combinations of various methods for dimensionality reduction, consensus functions and classification algorithms incorporated in our multistage scheme. The results of the experiments demonstrate that the highest precision, recall and F-measure are achieved by the combination of the rank correlation coefficient for dimensionality reduction, HBGF consensus function and the SMO classifier with the polynomial kernel.     

基于成对约束半监督降维的高光谱遥感影像特征提取

半监督降维(Semi\|Supervised Dimensionality Reduction,SSDR)框架下,基于成对约束提出一种半监督降维算法SCSSDR。利用成对样本进行构图,在保持局部结构的同时顾及数据的全局结构。通过最优化目标函数,使得同类样本更加紧凑\,异类样本更加离散。采用UCI数据集对算法进行定量分析,发现该方法优于PCA及传统流形学习算法,进一步的UCI数据集和高光谱数据集分类实验表明:该方法适合于进行分类目的特征提取。     

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications. Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension. The classification accuracy of hyperspectral images (HSI) increases significantly by employing both spatial and spectral features. For this work, the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared (VNIR) range of 400 to 1000 nm wavelength within 180 spectral bands. The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel. The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system. In this study, a unique pixel-based approach was designed to improve the crops' classification accuracy by using the edge-preserving features (EPF) and principal component analysis (PCA) in conjunction. The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI. In the second step, this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information. The resultant feature space (PCA-EPF) demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost. The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF. The classification performance evaluation was measured in terms of individual class accuracy, overall accuracy, average accuracy, and Cohen kappa factor. The proposed scheme achieved greater than 90 % results for all the performance evaluation metrics. The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range. The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.     

基于非线性降维多项式逻辑斯蒂回归的图像/非图像数据的分类与识别

在面向大规模复杂数据的模式分类和识别问题中,绝大多数的分类器都遇到了维数灾难这一棘手的问题.在进行高维数据分类之前,基于监督流形学习的非线性降维方法可提供一种有效的解决方法.利用多项式逻辑斯蒂回归方法进行分类预测,并结合基于非线性降维的非监督流形学习方法解决图像以及非图像数据的分类问题,因而形成了一种新的分类识别方法.大量的实验测试和比较分析验证了本文所提方法的优越性.     

Kernel logistic PLS: A tool for supervised nonlinear dimensionality reduction and binary classification

“Kernel logistic PLS” (KL-PLS) is a new tool for supervised nonlinear dimensionality reduction and binary classification. The principles of KL-PLS are based on both PLS latent variables construction and learning with kernels. The KL-PLS algorithm can be seen as a supervised dimensionality reduction (complexity control step) followed by a classification based on logistic regression. The algorithm is applied to 11 benchmark data sets for binary classification and to three medical problems. In all cases, KL-PLS proved its competitiveness with other state-of-the-art classification methods such as support vector machines. Moreover, due to successions of regressions and logistic regressions carried out on only a small number of uncorrelated variables, KL-PLS allows handling high-dimensional data. The proposed approach is simple and easy to implement. It provides an efficient complexity control by dimensionality reduction and allows the visual inspection of data segmentation.     

利用PCA进行深度学习图像特征提取后的降维研究

深度学习是当前人工智能领域广泛使用的一种机器学习方法.深度学习对数据的高度依赖性使得数据需要处理的维度剧增,极大地影响了计算效率和数据分类性能.本文以数据降维为研究目标,对深度学习中的各种数据降维方法进行分析.在此基础上,以Caltech 101图像数据集为实验对象,采用VGG-16深度卷积神经网络进行图像的特征提取,以PCA主成分分析方法为例来实现高维图像特征数据的降维处理.在实验阶段,采用欧氏距离作为相似性度量来检验经过降维处理后的精度指标.实验证明：当提取VGG-16神经网络fc3层的4096维特征后,使用PCA法将数据维度降至64维,依然能够保持较高的特征信息.     

自适应邻域选择的数据可分性降维方法

针对现有基于流形学习的降维方法对局部邻域大小选择的敏感性,且降至低维后的数据不具有很好的可分性,提出一种自适应邻域选择的数据可分性降维方法。该方法通过估计数据的本征维度和局部切方向来自适应地选择每一样本点的邻域大小;同时,使用映射数据时的聚类信息来汇聚相似的样本点,保证降维后的数据具有良好的可分性,使之实现更好的降维效果。实验结果表明,在人工生成的数据集上,新方法获得了较好的嵌入结果;并且在人脸的可视化分类和图像检索中得到了期望的结果。     

局部敏感的半监督鉴别分析方法

为解决图像处理中的高维特征在模式分类中带来的问题,提出一种基于半监督学习理论的数据降维方法,称为局部敏感的半监督鉴别分析算法.为能够发现局部的流形结构,算法寻找一个能够最小化类内距离的同时最大化类间距离的投影,并且在最优化过程中充分利用无标签数据,控制局部邻域的散度.在人脸识别数据库和行为数据库中的测试结果表明了该算法是有效的.     

基于最小分类错误率和Parzen窗的降维方法

提出了一种基于最小分类错误率和Parzen窗的降维方法,利用Parzen窗估计数据的概率密度分布;通过计算各特征维度下的分类错误率,判断该特征维度对目标分类的贡献度;依据贡献度大小进行特征维度选择从而达到降维的目的。     

User modeling: Through statistical analysis and subspace learning

One of the challenges which must be faced in the field of the information processing is the need to cope with huge amounts of data. There exist many different environments in which large quantities of information are produced. For example, in a command-line interface, a computer user types thousands of commands which can hide information about the behavior of her/his. However, processing this kind of streaming data on-line is a hard problem.This paper addresses the problem of the classification of streaming data from a dimensionality reduction perspective. We propose to learn a lower dimensionality input model which best represents the data and improves the prediction performance versus standard techniques. The proposed method uses maximum dependence criteria as distance measurement and finds the transformation which best represents the command-line user. We also make a comparison between the dimensionality reduction approach and using the full dataset. The results obtained give some deeper understanding in advantages and drawbacks of using both perspectives in this user classifying environment.     

一种降维新方法*

针对图像数据的72维HSV颜色特征,对数据集的本征维概念进行了有意义的扩展,在此基础上提出了一个新的降维机制.实验证明,该降维算法是行之有效的.     

基于局域主方向重构的适应性非线性维数约减

现有的主要非线性维数约减算法,如SIE和Isomap等,其邻域参数的设定是全局性的。仿真表明,对于局域流形结构差异较大的数据集,全局一致的邻域参数可能无法获得合理的嵌入结果。为此给出基于局域主方向重构的适应性邻域选择算法。算法首先为每个参考点选择一个邻域集,使各邻域集近似处于局域主线性子空间,并计算各邻域集的基向量集;再由基向量集对各邻域点的线性拟合误差判定该邻域点与主线性子空间的偏离程度,删除偏离较大的点。仿真表明,基于局域主方向重构的适应性邻域选择可有效处理局域流形结构差异较大的数据集;且相对于已有的适应性邻域选择算法,可以更好屏蔽靠近参考点的孤立噪声点及较大的空间曲率导致的虚假连通性。     

基于判别式扩散映射分析的非线性特征提取

针对高维数据难以被人们直观理解,且难以被机器学习和数据挖据算法有效地处理的问题,提出一种新的非线性降维方法--判别式扩散映射分析(DDMA)。该方法将判别核方案应用到扩散映射框架中,依据样本类别标签在类内窗宽和类间窗宽中判别选取高斯核窗宽,使核函数能够有效提取数据的关联特性,准确描述数据空间的结构特征。通过在人工合成Swiss-roll测试和青霉素发酵过程中的仿真应用,与主成分分析(PCA)、线性判别分析(LDA)、核主成分分析(KPCA)、拉普拉斯特征映射(LE)算法和扩散映射(DM)进行比较,实验结果表明DDMA方法在低维空间中代表高维数据的同时成功保留了数据的原始特性,且通过该方法在低维空间中产生的数据结构特性优于其他方法,在数据降维与特征提取性能上验证了该方案的有效性。     

A non-parametric clustering scheme for landsat

A 4-dimensional histogram is computed to reduce the large LANDSAT pixel data (up to 7.6 million pixels to a frame) to the much smaller number (6,000) of distinct vectors and their frequency of occurrence in the scene. The vectors are clustered by a recent non-parametric clustering algorithm⁽³⁾ using the histogram count as a probability density estimate. The resultant clusters are unimodal m the 4-dimensional histogram and can possess arbitrary shapes. The algorithm is non-iterative and does not require specification of the number of clusters a priori.

Hashing is used to generate the histogram and also subsequent table look-up classification of the individual pixels in the image after the histogram vectors are clustered. The resultant clustering scheme is very efficient and a 512 × 512 LANDSAT scene can be clustered in less than 2 min of CPU time on a PDP-10 computer. Results of the application of the clustering scheme on representative LANDSAT scenes are included.     

鲁棒的稀疏Lp-模主成分分析

主成分分析（Principle component analysis,PCA）是一种被广泛应用的降维方法.然而经典PCA的构造基于L₂-模导致了其对离群点和噪声点敏感,同时经典PCA也不具备稀疏性的特点.针对此问题,本文提出基于Lp-模的稀疏主成分分析降维方法（LpSPCA）.LpSPCA通过极大化带有稀疏正则项的Lp-模样本方差,使得其在降维的同时保证了稀疏性和鲁棒性.LpSPCA可用简单的迭代算法求解,并且当p≥1时该算法的收敛性可在理论上保证.此外通过选择不同的p值,LpSPCA可应用于更广泛的数据类型.人工数据及人脸数据上的实验结果表明,本文所提出的LpSPCA不仅具有较好的降维效果,并且具有较强的抗噪能力.     

Dimensionality reduction of image features using the canonical contextual correlation projection

A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classical linear discriminant analysis (LDA), extending this technique to cases where there is dependency between the output variables, i.e., the class labels, and not only between the input variables. (The latter can readily be dealt with in standard LDA.) The novel method is useful, for example, in supervised segmentation tasks in which high-dimensional feature vectors describe the local structure of the image.

The principal idea is that where standard LDA merely takes into account a single class label for every feature vector, the new technique incorporates class labels of its neighborhood in the analysis as well. In this way, the spatial class label configuration in the vicinity of every feature vector is accounted for, resulting in a technique suitable for, e.g. image data.

This extended LDA, that takes spatial label context into account, is derived from a formulation of standard LDA in terms of canonical correlation analysis. The novel technique is called the canonical contextual correlation projection (CCCP).

An additional drawback of LDA is that it cannot extract more features than the number of classes minus one. In the two-class case this means that only a reduction to one dimension is possible. Our contextual LDA approach can avoid such extreme deterioration of the classification space and retain more than one dimension.

The technique is exemplified on a pixel-based medical image segmentation problem in which it is shown that it may give significant improvement in segmentation accuracy.     

基于语义的文本特征加权分类算法

文本分类存在维数灾难、数据集噪声及特征词对分类贡献不同等问题,影响文本分类精度。为提高文本分类精度,在数据处理方面提出一种新方法。该方法首先对数据集进行去噪处理,结合特征提取算法和语义分析方法对数据实现降维,再利用词语语义相关度对文本特征向量中每个特征词赋予不同权重;并利用经过上述处理的文本数据学习分类器。实验结果表明,该文本处理方法能够有效提高文本分类精度。     

无监督混阶栈式稀疏自编码器的图像分类学习

目前多数图像分类的方法是采用监督学习或者半监督学习对图像进行降维,然而监督学习与半监督学习需要图像携带标签信息。针对无标签图像的降维及分类问题,提出采用混阶栈式稀疏自编码器对图像进行无监督降维来实现图像的分类学习。首先,构建一个具有三个隐藏层的串行栈式自编码器网络,对栈式自编码器的每一个隐藏层单独训练,将前一个隐藏层的输出作为后一个隐藏层的输入,对图像数据进行特征提取并实现对数据的降维。其次,将训练好的栈式自编码器的第一个隐藏层和第二个隐藏层的特征进行拼接融合,形成一个包含混阶特征的矩阵。最后,使用支持向量机对降维后的图像特征进行分类,并进行精度评价。在公开的四个图像数据集上将所提方法与七个对比算法进行对比实验,实验结果表明,所提方法能够对无标签图像进行特征提取,实现图像分类学习,减少分类时间,提高图像的分类精度。     

基于子空间学习的图稀疏属性选择算法*

针对处理高维度属性的大数据的属性约减方法进行了研究。发现属性选择和子空间学习是属性约简的两种常见方法,其中属性选择具有很好的解释性,子空间学习的分类效果优于属性选择。而往往这两种方法是各自独立进行应用。为此,提出了综合这两种属性约简方法,设计出新的属性选择方法。即利用子空间学习的两种技术(即线性判别分析(LDA)和局部保持投影(LPP)),考虑数据的全局特性和局部特性,同时设置稀疏正则化因子实现属性选择。基于分类准确率、方差和变异系数等评价指标的实验结果比较,表明该算法相比其它对比算法,能更有效的选取判别属性,并能取得很好的分类效果。