Multiple kernel-based multi-instance learning algorithm for image classification

In this paper, a novel multi-instance learning (MIL) algorithm based on multiple-kernels (MK) framework has been proposed for image classification. This newly developed algorithm defines each image as a bag, and the low-level visual features extracted from its segmented regions as instances. This algorithm is started from constructing a “word-space” from instances based on a collection of “visual-words” generated by affinity propagation (AP) clustering method. After calculating the distance between a “visual-word” and the bag (image), a nonlinear mapping mechanism is introduced for registering each bag as a coordinate point in the “word-space”. In this case, the MIL problem is transformed into a standard supervised learning problem, which allows multiple-kernels support vector machine (MKSVM) classifiers to be trained for the image categorization. Compared with many popular MIL algorithms, the proposed method, named as MKSVM-MIL, shows its satisfactorily experimental results on the COREL dataset, which highlights the robustness and effectiveness for image classification applications.     

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.     

A novel semi-supervised canonical correlation analysis and extensions for multi-view dimensionality reduction

Canonical correlation analysis (CCA) is an efficient method for dimensionality reduction on two-view data. However, as an unsupervised learning method, CCA cannot utilize partly given label information in multi-view semi-supervised scenarios. In this paper, we propose a novel two-view semi-supervised learning method, called semi-supervised canonical correlation analysis based on label propagation (LPbSCCA). LPbSCCA incorporates a new sparse representation based label propagation algorithm to infer label information for unlabeled data. Specifically, it firstly constructs dictionaries consisting of all labeled samples; and then obtains reconstruction coefficients of unlabeled samples using sparse representation technique; at last, by combining given labels of labeled samples, estimates label information for unlabeled ones. After that, it constructs soft label matrices of all samples and probabilistic within-class scatter matrices in each view. Finally, in order to enhance discriminative power of features, it is formulated to maximize the correlations between samples of the same class from cross views, while minimizing within-class variations in the low-dimensional feature space of each view simultaneously. Furthermore, we also extend a general model called LPbSMCCA to handle data from multiple (more than two) views. Extensive experimental results from several well-known datasets demonstrate that the proposed methods can achieve better recognition performances and robustness than existing related methods.     

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.     

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.     

支持向量分类和多宽度高斯核

支持向量分类中,高斯核不区分样本中各个特征的重要性,显然各个特征对分类的贡献一般是不相同的.为了体现这种差别从而提高支持向量机的泛化性能,文中提出了多宽度高斯核的概念.多宽度高斯核增加了支持向量机的超级参数,进一步地,文中提出了多参数模型选择算法.算法利用误差界自动实现模型选择.通过实验验证了多宽度高斯核和多参数模型选择算法的有效性.     

基于改进的有效区域基因选择与跨模态语义挖掘的图像属性标注

图像属性标注是一种更细化的图像标注，它能缩小认知与特征间"语义鸿沟".现有研究多基于单特征且未挖掘属性蕴含的深层语义，故无法准确刻画图像内容.改进有效区域基因选择算法融合图像特征，并设计迁移学习策略，实现材质属性标注；基于判别相关分析挖掘特征间跨模态语义，以改进相对属性模型，标注材质属性蕴含的深层语义-实用属性.实验表明：材质属性标注精准度达63.11%，较最强基线提升1.97%；实用属性标注精准度达59.15%，较最强基线提升2.85%；层次化的标注结果能全面刻画图像内容.     

Semi-supervised feature selection with exploiting shared information among multiple tasks

Given several related tasks, multi-task feature selection determines the importance of features by mining the correlations between them. There have already many efforts been made on the supervised multi-task feature selection. However, in real-world applications, it’s noticeably time-consuming and unpractical to collect sufficient labeled training data for each task. In this paper, we propose a novel feature selection algorithm, which integrates the semi-supervised learning and multi-task learning into a joint framework. Both the labeled and unlabeled samples are sufficiently utilized for each task, and the shared information between different tasks is simultaneously explored to facilitate decision making. Since the proposed objective function is non-smooth and difficult to be solved, we also design an efficient iterative algorithm to optimize it. Experimental results on different applications demonstrate the effectiveness of our algorithm.