Multi-task Joint Sparse Representation Classification Based on Fisher Discrimination Dictionary Learning

Recently,sparse representation classification(SRC)and fisher discrimination dictionary learning(FDDL)methods have emerged as important methods for vehicle classification.In this paper,inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection,we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors.To improve the classification accuracy in complex scenes,we develop a new method,called multi-task joint sparse representation classification based on fisher discrimination dictionary learning,for vehicle classification.In our proposed method,the acoustic and seismic sensor data sets are captured to measure the same physical event simultaneously by multiple heterogeneous sensors and the multi-dimensional frequency spectrum features of sensors data are extracted using Mel frequency cepstral coefficients(MFCC).Moreover,we extend our model to handle sparse environmental noise.We experimentally demonstrate the benefits of joint information fusion based on fisher discrimination dictionary learning from different sensors in vehicle classification tasks.

Hyperspectral image classification based on spatial and spectral features and sparse representation

To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is based on the Gabor spatial texture features and nonparametric weighted spectral features, and the sparse representation classification method（Gabor–NWSF and SRC）, abbreviated GNWSF–SRC. The proposed（GNWSF–SRC） method first combines the Gabor spatial features and nonparametric weighted spectral features to describe the hyperspectral image, and then applies the sparse representation method. Finally, the classification is obtained by analyzing the reconstruction error. We use the proposed method to process two typical hyperspectral data sets with different percentages of training samples. Theoretical analysis and simulation demonstrate that the proposed method improves the classification accuracy and Kappa coefficient compared with traditional classification methods and achieves better classification performance.

Weighted Sparse Image Classification Based on Low Rank Representation

The conventional sparse representation-based image classification usually codes the samples independently,which will ignore the correlation information existed in the data.Hence,if we can explore the correlation information hidden in the data,the classification result will be improved significantly.To this end,in this paper,a novel weighted supervised spare coding method is proposed to address the image classification problem.The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation.And then,it introduced the extracted structural information to a novel weighted sparse representation model to code the samples in a supervised way.Experimental results show that the proposed method is superiority to many conventional image classification methods.

Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization for Hyperspectral Image Classification

Low-Rank and Sparse Representation(LRSR)method has gained popularity in Hyperspectral Image(HSI)processing.However,existing LRSR models rarely exploited spectral-spatial classification of HSI.In this paper,we proposed a novel Low-Rank and Sparse Representation with Adaptive Neighborhood Regularization(LRSR-ANR)method for HSI classification.In the proposed method,we first represent the hyperspectral data via LRSR since it combines both sparsity and low-rankness to maintain global and local data structures simultaneously.The LRSR is optimized by using a mixed Gauss-Seidel and Jacobian Alternating Direction Method of Multipliers(M-ADMM),which converges faster than ADMM.Then to incorporate the spatial information,an ANR scheme is designed by combining Euclidean and Cosine distance metrics to reduce the mixed pixels within a neighborhood.Lastly,the predicted labels are determined by jointly considering the homogeneous pixels in the classification rule of the minimum reconstruction error.Experimental results based on three popular hyperspectral images demonstrate that the proposed method outperforms other related methods in terms of classification accuracy and generalization performance.

Metasample-Based Robust Sparse Representation for Tumor Classification

In this paper, based on sparse representation classification and robust thought, we propose a new classifier, named MRSRC (Metasample Based Robust Sparse Representation Classificatier), for DNA microarray data classification. Firstly, we extract Metasample from trainning sample. Secondly, a weighted matrix W is added to solve an l1-regular- ized least square problem. Finally, the testing sample is classified according to the sparsity coefficient vector of it. The experimental results on the DNA microarray data classification prove that the proposed algorithm is efficient.

Integrating absolute distances in collaborative representation for robust image classification

Conventional sparse representation based classification （SRC） represents a test sample with the coefficient solved by each training sample in all classes. As a special version and improvement to SRC, collaborative representation based classification （CRC） obtains representation with the contribution from all training samples and produces more promising results on facial image classification. In the solutions of representation coefficients, CRC considers original value of contributions from all samples. However, one prevalent practice in such kind of distance-based methods is to consider only absolute value of the distance rather than both positive and negative values. In this paper, we propose an novel method to improve collaborative representation based classification, which integrates an absolute distance vector into the residuals solved by collaborative representation. And we named it AbsCRC. The key step in AbsCRC method is to use factors a and b as weight to combine CRC residuals rescrc with absolute distance vector disabs and generate a new dviaetion r = a·rescrc b.disabs, which is in turn used to perform classification. Because the two residuals have opposite effect in classification, the method uses a subtraction operation to perform fusion. We conducted extensive experiments to evaluate our method for image classification with different instantiations. The experimental results indicated that it produced a more promising result of classification on both facial and non-facial images than original CRC method.

A new discriminative sparse parameter classifier with iterative removal for face recognition

Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typical representative.However,CRC cannot distinguish similar samples well,leading to a wrong classification easily.As an improved method based on CRC,the two-phase test sample sparse representation(TPTSSR)removes the samples that make little contribution to the representation of the testing sample.Nevertheless,only one removal is not sufficient,since some useless samples may still be retained,along with some useful samples maybe being removed randomly.In this work,a novel classifier,called discriminative sparse parameter(DSP)classifier with iterative removal,is proposed for face recognition.The proposed DSP classifier utilizes sparse parameter to measure the representation ability of training samples straight-forward.Moreover,to avoid some useful samples being removed randomly with only one removal,DSP classifier removes most uncorrelated samples gradually with iterations.Extensive experiments on different typical poses,expressions and noisy face datasets are conducted to assess the performance of the proposed DSP classifier.The experimental results demonstrate that DSP classifier achieves a better recognition rate than the well-known SRC,CRC,RRC,RCR,SRMVS,RFSR and TPTSSR classifiers for face recognition in various situations.

Robust Hierarchical Framework for Image Classification via Sparse Representation

The sparse representation-based classification algorithm has been used for human face recognition. But an image database was restricted to human frontal faces with only slight illumination and expression changes. Cropping and normalization of the face needs to be done beforehand. This paper uses a sparse representation-based algorithm for generic image classification with some intra-class variations and background clutter. A hierarchical framework based on the sparse representation is developed which flexibly combines different global and local features. Experiments with the hierarchical framework on 25 object categories selected from the Caltech101 dataset show that exploiting the advantage of local features with the hierarchical framework improves the classification performance and that the framework is robust to image occlusions, background clutter, and viewpoint changes.

Discriminative Structured Dictionary Learning for Image Classification

In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary's discriminative power, the reconstruction error, classification error and inhomogeneous representation error are integrated into the objective function. The proposed approach learns a single structured dictionary and a linear classifier jointly. The learned dictionary encourages the samples from the same class to have similar sparse codes, and the samples from different classes to have dissimilar sparse codes. The solution to the objective function is achieved by employing a feature-sign search algorithm and Lagrange dual method. Experimental results on three public databases demonstrate that the proposed approach outperforms several recently proposed dictionary learning techniques for classification.

Semi-Supervised Dimensionality Reduction of Hyperspectral Image Based on Sparse Multi-Manifold Learning

In this paper, we proposed a new semi-supervised multi-manifold learning method, called semi- supervised sparse multi-manifold embedding (S3MME), for dimensionality reduction of hyperspectral image data. S3MME exploits both the labeled and unlabeled data to adaptively find neighbors of each sample from the same manifold by using an optimization program based on sparse representation, and naturally gives relative importance to the labeled ones through a graph-based methodology. Then it tries to extract discriminative features on each manifold such that the data points in the same manifold become closer. The effectiveness of the proposed multi-manifold learning algorithm is demonstrated and compared through experiments on a real hyperspectral images.

Spatial-Aware Supervised Learning for Hyper-Spectral Image Classification Comprehensive Assessment

A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.

基于自适应矩阵的核联合稀疏表示高光谱图像分类

针对高光谱图像丰富的空间信息和光谱信息未充分利用的问题,提出了基于自适应矩阵的核联合稀疏表示高光谱图像分类的方法。在特征表示阶段,定义了自适应矩阵特征,通过结合自适应邻域块策略与非线性相关熵度量构成的特征来描述原始光谱像素,充分融合了形状可变的空间信息与非线性光谱信息。在分类阶段,考虑自适应矩阵和高光谱图像非线性,采用对数欧式核函数,构建了核联合稀疏表示模型,以获得重构误差。同时利用字典空间信息构建了矩阵相关性,引入平衡参数实现了稀疏重构误差与矩阵相关性的联合分类。在两个数据集上的实验结果表明,该算法充分利用了高光谱图像的空间信息、光谱信息,能够有效提高分类精度。

多源声发射信号混合重叠组稀疏分类研究

针对高速列车车体裂纹声发射检测的多源、波模式重叠及噪声干扰问题,提出一种基于本征模态的混合重叠组稀疏(MOGS)分类方法用于声发射源识别。MOGS是一种兼顾组间和组内稀疏,同时允许类间特征重叠的结构稀疏模型。设计了一种新的噪声预分解矩阵以降低本征模态分解计算量,选取目标特征频带模态为分类样本来提高类间差异。通过K-SVD层次稀疏组套索罚训练MOGS类别字典,并给出一种罚函数块坐标可分离的近似光滑处理过程以实现MOGS套索求解。实验表明,该方法对几类多源含噪信号分类准确率均高于80%,在识别率和波形重构效果上优于对比方法。

联合核稀疏表示和增强字典的SAR目标识别方法

为提高合成孔径雷达(SAR)图像目标识别性能,以传统稀疏表示分类(SRC)为基础,提出联合核稀疏表示分类(KSRC)和增强字典的方法。KSRC在SRC的基础上引入非线性核函数,从而提升分类器对于非线性数据关系的表征能力。增强字典在原始训练样本的基础上,通过噪声添加和部分遮挡扩展原始字典,提升其对典型扩展操作条件的适应能力。同时,增强字典在KSRC的作用下,可以进一步提升对其他相关扩展操作条件的覆盖程度,从而提升识别方法对于多类扩展操作条件的有效性。以MSTAR数据集为基础开展实验,设置了标准操作条件以及噪声干扰、部分遮挡、型号差异等扩展操作条件,实验结果显示了本文方法的优势性能。

利用潜在稀疏表示学习的增强局部保持投影方法

降维在机器学习和模式识别领域中起着至关重要的作用.目前,现有的基于投影的方法往往只单一地利用了数据之间的距离信息或表示关系来保持数据的结构,难以有效捕捉高维空间中数据流形的非线性特征和复杂相关性.为了解决这个问题,本文提出了一种利用潜在稀疏表示学习的增强局部保持投影(enhanced locality preserving projection with latent sparse representation learning,LPP_SRL)方法.所提出方法不仅利用距离信息以保留数据的局部结构,而且利用多重局部线性表示来揭示数据的全局非线性结构.此外,为了在投影学习和稀疏自表示之间建立联系,本文采用了一种新策略,将稀疏自表示中的字典替换为低维表示的重构样本.通过这种方法,能够有效地过滤掉不相关的特征和噪声,从而更好地保留原始特征空间中的主要成分.在多个公开可用的基准数据集上进行的大量实验证明了所提出方法的有效性和优越性.

Weighted average integration of sparse representation and collaborative representation for robust face recognition

Sparse representation is a significant method to perform image classification for face recognition. Sparsity of the image representation is the key factor for robust image classification.As an improvement to sparse representation-based classification, collaborative representation is a newer method for robust image classification. Training samples of all classes collaboratively contribute together to represent one single test sample. The ways of representing a test sample in sparse representation and collaborative representation are very different, so we propose a novel method to integrate both sparse and collaborative representations to provide improved results for robust face recognition.The method first computes a weighted average of the representation coefficients obtained from two conventional algorithms, and then uses it for classification. Experiments on several benchmark face databases show that our algorithm outperforms both sparse and collaborative representation-based classification algorithms, providing at least a 10%improvement in recognition accuracy.

基于改进稀疏表示的海上风电场交流海底电缆短路故障分类方法

准确、快速的海底电缆故障分类是海上风电场运维的重要一环。该文提出一种基于改进稀疏表示的海上风电场交流海底电缆短路故障分类方法,该方法综合利用故障发生后半周波电流信号的时域特征作为故障分类依据,采用K次奇异值分解(K singular value decomposition,K-SVD)字典学习算法对各类故障信号的特征信息进行学习,构造出准确匹配各类故障本质特征的过完备字典。在学习字典的基础上,提出一种基于混合交替方向乘子法(mixed alternating direction method of multipliers,M-ADMM)的改进稀疏分解算法将故障信号分解为过完备字典与稀疏向量的乘积,结合基于稀疏表示的分类方法实现对故障重构信号的分类。仿真研究结果表明,该改进稀疏分解算法具有精确的信号重构、降噪效果。所提出的故障分类方法无需人工构造故障信号特征,避免了多工况故障信号特征筛选、时频域变换等繁琐流程。与SVM、CNN、LSTM等智能分类算法的对比结果表明,该方法具有较强自适应性的同时不易受故障时刻、故障位置影响且噪声鲁棒性强,可以准确识别海底电缆场景下低阻短路故障类型。

结合改进LBP和SRC的高光谱图像分类研究

针对传统局部二值模型(local binary pattern,LBP)提取高光谱图像纹理特征信息量庞大的难题,提出一种基于对称旋转不变等价局部二值模型(symmetrical rotation invariant uniform LBP,SRIULBP)的高光谱图像特征提取方法,以缩减特征维度;针对稀疏表示分类(sparse representation classification,SRC)模型中稀疏字典冗余的缺陷,采用近邻思想,提出最近邻稀疏表示(nearest neighbor SRC,NNSRC)分类方法,实现高光谱图像的高效、高准确度分类。数据实验结合表明,SRIULBP能快速提取图像特征,提出的分类方法不仅在分类精度上优于其他稀疏表示分类算法,并且具有更强的时效性与泛化能力。

基于多超图融合的超图神经网络模型构建及阿尔茨海默病分类

针对目前超图神经网络构建方法单一化,导致被试特征间的交互信息无法表征,从而影响超图神经网络模型分类性能的问题。提出一种多超图融合技术,融合多个超图为一个超图,从而互补多个超图各自所表征的高阶特征,以此来提高超图神经网络模型的分类性能。具体来说,基于结构磁共振成像数据,使用基于稀疏表示的最小绝对收缩和选择算法(least absolute shrinkage and selection operator,LASSO)方法,稀疏组LASSO方法以及覆盖组LASSO方法进行超图构建,然后分别基于超图融合技术将三个单一超图进行融合。接着基于融合的超图,构建超图神经网络模型,最终用于阿尔兹海默症及轻度认知障碍的分类。实验结果表明,本文所提方法的分类准确率达到79.21%,证明了该方法在阿尔兹海默症及轻度认知障碍的分类有较高的准确性和泛化性。

基于表示模型的高光谱遥感影像分类综述

高光谱成像技术具有光谱连续、图谱合一的特点,可实现地物目标的精细化解译。其中影像分类是高光谱遥感图像信息处理领域的前沿科学问题。表示模型在影像分类方面具有较大优势,近年来受到了广泛的关注和研究,取得了一系列成果。基于此,本文首先介绍了稀疏表示和协同表示模型的原理;其次系统地阐述了高光谱遥感影像分类中稀疏表示和协同表示的研究现状;最后对该研究领域发展提出建议和展望。