Kernel propagation strategy: A novel out-of-sample propagation projection for subspace learning

Kernel matrix optimization (KMO) aims at learning appropriate kernel matrices by solving a certain optimization problem rather than using empirical kernel functions. Since KMO is difficult to compute out-of-sample projections for kernel subspace learning, we propose a kernel propagation strategy (KPS) based on data distribution similar principle to effectively extract out-of-sample low-dimensional features for subspace learning with KMO. With KPS, we further present an example algorithm, i.e., kernel propagation canonical correlation analysis (KPCCA), which naturally fuses semi-supervised kernel matrix learning and canonical correlation analysis by means of kernel propagation projections. In KPCCA, the extracted correlation features of out-of-sample data not only incorporate integral data distribution information but also supervised information. Extensive experimental results have demonstrated the superior performance of our proposed method.     

Multi-patch embedding canonical correlation analysis for multi-view feature learning

Locality-based feature learning for multi-view data has received intensive attention recently. As a result of only considering single-category local neighbor relationships, most of such the learning methods are difficult to well reveal intrinsic geometric structure information of raw high-dimensional data. To solve the problem, we propose a novel supervised multi-view correlation feature learning algorithm based on multi-category local neighbor relationships, called multi-patch embedding canonical correlation analysis (MPECCA). Our algorithm not only employs multiple local patches of each raw data to better capture the intrinsic geometric structure information, but also makes intraclass correlation features as close as possible by minimizing intraclass scatter of each view. Extensive experimental results on several real-world image datasets have demonstrated the effectiveness of our algorithm.     

An orthogonal regularized CCA learning algorithm for feature fusion

Canonical correlation analysis (CCA) aims at extracting statistically uncorrelated features via conjugate orthonormalization constraints of the projection directions. However, the formulated directions under conjugate orthonormalization are not reliable when the training samples are few and the covariance matrix has not been exactly estimated. Additionally, this widely pursued property is focused on data representation rather than task discrimination. It is not suitable for classification problems when the samples that belong to different classes do not share the same distribution type. In this paper, an orthogonal regularized CCA (ORCCA) is proposed to avoid the above questions and extract more discriminative features via orthogonal constraints and regularized parameters. Experimental results on both handwritten numerals and face databases demonstrate that our proposed method significantly improves the recognition performance.     

基于半监督稀疏流形嵌入的高光谱影像特征提取

高光谱影像具有波段数多、冗余度高的特点,因此特征提取成为高光谱影像分类的研究热点。针对此问题,该文提出一种半监督稀疏流形嵌入(S3ME)算法,该方法充分利用标记样本和无标记样本,通过基于切空间的稀疏流形表示来自适应地揭示数据间的相似关系,并利用稀疏系数构建一个半监督相似图。在此基础上,增加了图中同类标记样本的权重,然后在低维空间中保持图的相似关系不变,并最小化加权距离和,获得投影矩阵实现特征提取。S3ME方法不仅能揭示数据间的稀疏流形结构,而且增强了同类数据的集聚性,能有效提取出鉴别特征,改善分类效果。该文提出的S3ME方法在PaviaU和Salinas高光谱数据集上的总体分类精度分别达到84.62%和88.07%,相比传统特征提取方法提升了地物分类性能。     

基于典型相关性分析的稀疏表示目标追踪

传统稀疏表示目标追踪算法首先通过粒子滤波方法对状态粒子进行采样,然后利用灰度特征表征采样粒子观测向量,最后构造基于观测向量的稀疏表示模型来进行目标追踪。与传统稀疏表示模型不同,该文提出一个基于典型相关性分析的稀疏表示模型,此模型首先使用两种特征来表征粒子观测向量,然后对两种观测向量的子空间投影结果进行稀疏建模。所构建的模型可通过在子空间中探究特征间的相关性来实现不同特征的互补融合,提升稀疏表示模型在复杂监控环境下的鲁棒性。     

Linear collaborative discriminant regression classification for face recognition

This paper proposes a novel face recognition method that improves Huang’s linear discriminant regression classification (LDRC) algorithm. The original work finds a discriminant subspace by maximizing the between-class reconstruction error and minimizing the within-class reconstruction error simultaneously, where the reconstruction error is obtained using Linear Regression Classification (LRC). However, the maximization of the overall between-class reconstruction error is easily dominated by some large class-specific between-class reconstruction errors, which makes the following LRC erroneous. This paper adopts a better between-class reconstruction error measurement which is obtained using the collaborative representation instead of class-specific representation and can be regarded as the lower bound of all the class-specific between-class reconstruction errors. Therefore, the maximization of the collaborative between-class reconstruction error maximizes each class-specific between-class reconstruction and emphasizes the small class-specific between-class reconstruction errors, which is beneficial for the following LRC. Extensive experiments are conducted and the effectiveness of the proposed method is verified.     

Semi-supervised classification via discriminative sparse manifold regularization

In this paper, a newly semi-supervised manifold learning algorithm named Discriminative Sparse Manifold Regularization (DSMR) is proposed. In DSMR, the whole unlabeled sample set is used to reconstruct the mean vector of each class, then obtains the sparse coefficient. For each sample of labeled samples, the new dictionary is composed of samples from the same class and the samples from the unlabeled sample set according to the corresponding rows of the sparse coefficient. For each unlabeled sample, the new dictionary is composed of samples from the whole unlabeled samples and the samples from the labeled class according to the corresponding columns of the sparse coefficient. Additionally, a discriminative term is added to stabilize performance of the algorithm. Extensive experiments on the several UCI datasets and face datasets demonstrate the effectiveness of the proposed DSMR.     

Kernel dictionary learning based discriminant analysis

Sparse representation based classification (SRC) has been successfully applied in many applications. But how to determine appropriate features that can best work with SRC remains an open question. Dictionary learning (DL) has played an import role in the success of sparse representation, while SRC treats the entire training set as a structured dictionary. In addition, as a linear algorithm, SRC cannot handle the data with highly nonlinear distribution. Motivated by these concerns, in this paper, we propose a novel feature learning method (termed kernel dictionary learning based discriminant analysis, KDL-DA). The proposed algorithm aims at learning a projection matrix and a kernel dictionary simultaneously such that in the reduced space the sparse representation of the data can be easily obtained, and the reconstruction residual can be further reduced. Thus, KDL-DA can achieve better performances in the projected space. Extensive experimental results show that our method outperforms many state-of-the-art methods.     

Semi-supervised Software Defect Prediction Using Task-Driven Dictionary Learning

We present a semi-supervised approach for software defect prediction.The proposed method is designed to address the special problematic characteristics of software defect datasets,namely,lack of labeled samples and class-imbalanced data.To alleviate these problems,the proposed method features the following components.Being a semi-supervised approach,it exploits the wealth of unlabeled samples in software systems by evaluating the confidence probability of the predicted labels,for each unlabeled sample.And we propose to jointly optimize the classifier parameters and the dictionary by a task-driven formulation,to ensure that the learned features (sparse code) are optimal for the trained classifier.Finally,during the dictionary learning process we take the different misclassification costs into consideration to improve the prediction performance.Experimental results demonstrate that our method outperforms several representative stateof-the-art defect prediction methods.     

Intrinsic structure based feature transform for image classification

Most dimensionality reduction works construct the nearest-neighbor graph by using Euclidean distance between images; this type of distance may not reflect the intrinsic structure. Different from existing methods, we propose to use sets as input rather than single images for accurate distance calculation. The set named as neighbor circle consists of the corresponding data point and its neighbors in the same class. Then a supervised dimensionality reduction method is developed, i.e., intrinsic structure feature transform (ISFT), it captures the local structure by constructing the nearest-neighbor graph using the Log-Euclidean distance as measurements of neighbor circles. Furthermore, ISFT finds representative images for each class; it captures the global structure by using the projected samples of these representatives to maximize the between-class scatter measure. The proposed method is compared with several state-of-the-art dimensionality reduction methods on various publicly available databases. Extensive experimental results have demonstrated the effectiveness of the proposed approach.     

Color image canonical correlation analysis for face feature extraction and recognition

Canonical correlation analysis (CCA) is a powerful statistical analysis technique, which can extract canonical correlated features from two data sets. However, it cannot be directly used for color images that are usually represented by three data sets, i.e., red, green and blue components. Current multi-set CCA (mCCA) methods, on the other hand, can only provide the iterative solutions, not the analytical solutions, when processing multiple data sets. In this paper, we develop the CCA technique and propose a color image CCA (CICCA) approach, which can extract canonical correlated features from three color components and provide the analytical solution. We show the mathematical model of CICCA, prove that CICCA can be cast as solving three eigen-equations, and present the realization algorithm of CICCA. Experimental results on the AR and FRGC-2 public color face image databases demonstrate that CICCA outperforms several representative color face recognition methods.     

Multimedia annotation via semi-supervised shared-subspace feature selection

With the rapid development of social network and computer technologies, we always confront with high-dimensional multimedia data. It is time-consuming and unrealistic to organize such a large amount of data. Most existing methods are not appropriate for large-scale data due to their dependence of Laplacian matrix on training data. Normally, a given multimedia sample is usually associated with multiple labels, which are inherently correlated to each other. Although traditional methods could solve this problem by translating it into several single-label problems, they ignore the correlation among different labels. In this paper, we propose a novel semi-supervised feature selection method and apply it to the multimedia annotation. Both labeled and unlabeled samples are sufficiently utilized without the need of graph construction, and the shared information between multiple labels is simultaneously uncovered. We apply the proposed algorithm to both web page and image annotation. Experimental results demonstrate the effectiveness of our method.