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Abstract: In this paper we propose a concavely regularized convex relaxation based graph matching algorithm. The graph matching problem is firstly formulated as a constrained convex quadratic program by relaxing the feasible set from the permutation matrices to doubly stochastic matrices. To gradually push the doubly stochastic matrix back to be a permutation one, an objective function is constructed by adding a simple weighted concave regularization to the convex relaxation. By gradually increasing the weight of the concave term, minimization of the objective function will gradually push the doubly stochastic matrix back to be a permutation one. A concave–convex procedure (CCCP) together with the Frank–Wolfe algorithm is adopted to minimize the objective function. The algorithm can be used on any types of graphs and exhibits a comparable performance as the PATH following algorithm, a state-of-the-art graph matching algorithm but applicable only on undirected graphs. [Copyright &y& Elsevier]

Abstract: Principal component analysis (PCA) is widely used in recently proposed manifold learning algorithms to provide approximate local tangent spaces. However, such approximations provided by PCA may be inaccurate when local neighborhoods of the data manifold do not lie in or close to a linear subspace. Furthermore, the approximated tangent spaces can not fit the change in data distribution density. In this paper, a new method is proposed for providing faithful approximations to the local tangent spaces of a data manifold, which is proved to be more accurate than PCA. With this new method, an improved local tangent space alignment (ILTSA) algorithm is developed, which can efficiently recover the geometric structure of data manifolds even in the case when data are sparse or non-uniformly distributed. Experimental results are presented to illustrate the better performance of ILTSA on both synthetic data and image data. [ABSTRACT FROM AUTHOR]

An immune optimization algorithm is proposed in this paper based on the immune negative selection. The algorithm NSIOA is motivated by the negative selection mechanism in biological immune recognition. Different from the existing immune optimization methods, NSIOA constantly removes the worst solutions to get the optimal solution. Considering that removal of poor members of a population might lead to the loss of design information that may actually help identify better solutions in the search space, the proposed NSIOA is designed to keep the diversity of antibodies while removing poor members, therefore the algorithm will converge to global optimal solution with high probability. The convergence property and the complexity of the algorithm have also been analyzed. To illustrate the efficiency of the algorithm is used in solving the travel salesman problem. The theoretical analysis and experimental results show that the algorithm is of a strong potential in solving practical problems. [Copyright &y& Elsevier]

Abstract: Support vector machines (SVMs) are a new and important tool in data classification. Recently much attention has been devoted to large scale data classifications where decomposition methods for SVMs play an important role. So far, several decomposition algorithms for SVMs have been proposed and applied in practice. The algorithms proposed recently and based on rate certifying pair/set provide very attractive features compared with many other decomposition algorithms. They converge not only with finite termination but also in polynomial time. However, it is difficult to reach a good balance between low computational cost and fast convergence. In this paper, we propose a new simple decomposition algorithm based on a new philosophy on working set selection. It has been proven that the working set selected by the new algorithm is a rate certifying set. Further, compared with the existing algorithms based on rate certifying pair/set, our algorithm provides a very good feature in combination of lower computational complexity and faster convergence. [Copyright &y& Elsevier]

Abstract: We develop an ellipse detection algorithm based on the multisets mixture learning (MML) that differs from the conventional Hough transform perspective. The algorithm developed has potential advantages in terms of noise resistance, incomplete ellipse detection, and detecting a multitude of ellipses. [Copyright &y& Elsevier]

Abstract: In many industrial applications, detection of workpieces is the prerequisite of the subsequent operations such as automatic grasping and assembly tasks. However, the detection of workpieces under challenging conditions such as occlusion and cluttered background is still an open problem, which needs better solutions and further investigations. In this paper, a part-based adaptive detection approach is proposed to deal with abovementioned problems. The whole workpiece template is automatically divided into multiple subtemplates, which are equipped with adjustable weights adjusted according to their discriminative abilities. Then the weight adjustment process and the object localization process are finally embedded in an optimization framework—Differential Evolution (DE), which finally leads to the detection of workpieces. Experimental results demonstrate the effectiveness and robust performance of the proposed algorithm under challenging conditions. [Copyright &y& Elsevier]

Abstract: Manifold regularization (MR) is a promising regularization framework for semi-supervised learning, which introduces an additional penalty term to regularize the smoothness of functions on data manifolds and has been shown very effective in exploiting the underlying geometric structure of data for classification. It has been shown that the performance of the MR algorithms depends highly on the design of the additional penalty term on manifolds. In this paper, we propose a new approach to define the penalty term on manifolds by the sparse representations instead of the adjacency graphs of data. The process to build this novel penalty term has two steps. First, the best sparse linear reconstruction coefficients for each data point are computed by the l 1-norm minimization. Secondly, the learner is subject to a cost function which aims to preserve the sparse coefficients. The cost function is utilized as the new penalty term for regularization algorithms. Compared with previous semi-supervised learning algorithms, the new penalty term needs less input parameters and has strong discriminative power for classification. The least square classifier using our novel penalty term is proposed in this paper, which is called the Sparse Regularized Least Square Classification (S-RLSC) algorithm. Experiments on real-world data sets show that our algorithm is very effective. [Copyright &y& Elsevier]

Facial landmark tracking often adopts static models to generically fit per frame of video. This is considered inappropriate since such models ignore the informative correlation between previous and current frames. Moreover, most of these methods fail to balance the speed and accuracy for video-based facial landmark tracking simultaneously. In this paper, we propose an efficient online framework for video-based face alignment, named Incrementally Cascaded Broad Learning framework (ICBL). ICBL aims to continuously enhance the prediction capability of tracking model for sequential data. It is capable of learning the spatial appearance on specific-person statistics from continuous facial frames and using such knowledge to incrementally tune a cascade of regressors in parallel. To achieve this goal, we approximate the facial shape space by sampling from a dynamic distribution which is continuously updated by person-specific statistics from the tracked facial frames. This dramatically facilitates cascade regression to incrementally update all cascade-regressors in parallel, thus allowing a fast update of the whole model. Furthermore, we successfully incorporate both the linear and non-linear mappings into our parallel cascade framework and introduce Broad Learning (BL) algorithm as a solution for them simultaneously. Experimental results on the most popular and large-scale benchmark for facial landmark tracking show highly competitive performance of proposed ICBL in comparisons with the state-of-the-arts. The code of our ICBL framework has been available from https://github.com/CaifengLiu/Facial-landmark-tracking-by-ICBL. [ABSTRACT FROM AUTHOR]

Graph matching is a fundamental problem in artificial intelligence and structural data processing. Hypergraph matching has recently become popular in the graph matching community. Existing hypergraph matching algorithms usually resort to the continuous methods, while the combinatorial nature of hypergraph matching is not well considered. Therefore in this paper, we propose a novel hypergraph matching algorithm by introducing the affinity tensor updating based graduated projection. Specifically, the hypergraph matching problem is first formulated as a combinatorial optimization problem in a high order polynomial form. Then this NP-hard problem is relaxed and interpreted in a probabilistic manner, which is approximately solved by iterative techniques. The updating of the affinity tensor is performed in each iteration, besides the updating of probabilistic assignment vector. Experimental results on both synthetic and real-world datasets witness the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Support vector machine (SVM) is a widely used classification technique. However, it is difficult to use SVMs to deal with very large data sets efficiently. Although decomposed SVMs (DSVMs) and core vector machines (CVMs) have been proposed to overcome this difficulty, they cannot be applied to online classification (or classification with learning ability) because, when new coming samples are misclassified, the classifier has to be adjusted based on the new coming misclassified samples and all the training samples. The purpose of this paper is to address this issue by proposing an online CVM classifier with adaptive minimum-enclosing-ball (MEB) adjustment, called online CVMs (OCVMs). The OCVM algorithm has two features: (1) many training samples are permanently deleted during the training process, which would not influence the final trained classifier; (2) with a limited number of selected samples obtained in the training step, the adjustment of the classifier can be made online based on new coming misclassified samples. Experiments on both synthetic and real-world data have shown the validity and effectiveness of the OCVM algorithm. [Copyright &y& Elsevier]