Search

This paper presents a novel unsupervised multi-source domain adaptation approach, named as coupled local–global adaptation (CLGA). At the global level, in order to maximize the adaptation ability, CLGA regards multiple domains as a unity, and jointly mitigates the gaps of both marginal and conditional distributions between source and target dataset. At the local level, with the intention of maximizing the discriminative ability, CLGA investigates the relationship among distinctive domains, and exploits both class and domain manifold structures embedded in data samples. We formulate both local and global adaptation in a concise optimization problem, and further derive an analytic solution for the objective function. Extensive evaluations verify that CLGA performs better than several existing methods not only in multi-source adaptation tasks but also in single source scenarios. [ABSTRACT FROM AUTHOR]

The presence of noise in real data sets can harm the predictive performance of machine learning algorithms. There are several noise filtering techniques whose goal is to improve the quality of the data in classification tasks. These techniques usually scan the data for noise identification in a preprocessing step. Nonetheless, this is a non-trivial task and some noisy data can remain unidentified, while safe data can also be removed. The bias of each filtering technique influences its performance on a particular data set. Therefore, there is no single technique that can be considered the best for all domains or data distribution and choosing a particular filter is not straightforward. Meta-learning has been largely used in the last years to support the recommendation of the most suitable machine learning algorithm(s) for a new data set. This paper presents a meta-learning recommendation system able to predict the expected performance of noise filters in noisy data identification tasks. For such, a meta-base is created, containing meta-features extracted from several corrupted data sets along with the performance of some noise filters when applied to these data sets. Next, regression models are induced from this meta-base to predict the expected performance of the investigated filters in the identification of noisy data. The experimental results show that meta-learning can provide a good recommendation of the most promising filters to be applied to new classification data sets. [ABSTRACT FROM AUTHOR]

Clustering is an essential tool in data mining research and applications. It is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. Several clustering techniques have been proposed and implemented, and most of them successfully find excellent quality or optimal clustering results in the domains mentioned earlier. However, there has been a gradual shift in the choice of clustering methods among domain experts and practitioners alike, which is precipitated by the fact that most traditional clustering algorithms still depend on the number of clusters provided a priori. These conventional clustering algorithms cannot effectively handle real-world data clustering analysis problems where the number of clusters in data objects cannot be easily identified. Also, they cannot effectively manage problems where the optimal number of clusters for a high-dimensional dataset cannot be easily determined. Therefore, there is a need for improved, flexible, and efficient clustering techniques. Recently, a variety of efficient clustering algorithms have been proposed in the literature, and these algorithms produced good results when evaluated on real-world clustering problems. This study presents an up-to-date systematic and comprehensive review of traditional and state-of-the-art clustering techniques for different domains. This survey considers clustering from a more practical perspective. It shows the outstanding role of clustering in various disciplines, such as education, marketing, medicine, biology, and bioinformatics. It also discusses the application of clustering to different fields attracting intensive efforts among the scientific community, such as big data, artificial intelligence, and robotics. This survey paper will be beneficial for both practitioners and researchers. It will serve as a good reference point for researchers and practitioners to design improved and efficient state-of-the-art clustering algorithms. • Provide an up-to-date comprehensive review of the different clustering techniques. • Highlight novel and most recent practical applications areas of clustering. • Provide a convenient research path for new researchers. • Help experts develop new algorithms for emerging challenges in the research area. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a redundant robot arm that is capable of learning to reach for targets in space in a self-organized fashion while avoiding obstacles. Self-generated movement commands that activate correlated visual, spatial and motor information are used to learn forward and inverse kinematic control models while moving in obstacle-free space using the Direction-to-Rotation Transform (DIRECT). Unlike prior DIRECT models, the learning process in this work was realized using an online Fuzzy ARTMAP learning algorithm. The DIRECT-based kinematic controller is fault tolerant and can handle a wide range of perturbations such as joint locking and the use of tools despite not having experienced them during learning. The DIRECT model was extended based on a novel reactive obstacle avoidance direction (DIRECT-ROAD) model to enable redundant robots to avoid obstacles in environments with simple obstacle configurations. However, certain configurations of obstacles in the environment prevented the robot from reaching the target with purely reactive obstacle avoidance. To address this complexity, a self-organized process of mental rehearsals of movements was modeled, inspired by human and animal experiments on reaching, to generate plans for movement execution using DIRECT-ROAD in complex environments. These mental rehearsals or plans are self-generated by using the Fuzzy ARTMAP algorithm to retrieve multiple solutions for reaching each target while accounting for all the obstacles in its environment. The key aspects of the proposed novel controller were illustrated first using simple examples. Experiments were then performed on real robot platforms to demonstrate successful obstacle avoidance during reaching tasks in real-world environments. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to present an efficient hardware architecture for generalized Hebbian algorithm (GHA). In the architecture, the principal component computation and weight vector updating of the GHA are operated in parallel, so that the throughput of the circuit can be significantly enhanced. In addition, the weight vector updating process is separated into a number of stages for lowering area costs and increasing computational speed. To show the effectiveness of the circuit, a texture classification system based on the proposed architecture is designed. It is embedded in a system-on-programmable-chip (SOPC) platform for physical performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance and low area costs. [Copyright &y& Elsevier]

Abstract: Hybrid strategy, which generalizes a specific single-label algorithm while one or two data decomposition tricks are applied implicitly or explicitly, has become an effective and efficient tool to design and implement various multi-label classification algorithms. In this paper, we extend traditional binary support vector machine by introducing an approximate ranking loss as its empirical loss term to build a novel support vector machine for multi-label classification, resulting into a quadratic programming problem with different upper bounds of variables to characterize label correlation of individual instance. Further, our optimization problem can be solved via combining one-versus-rest data decomposition trick with modified binary support vector machine, which dramatically reduces computational cost. Experimental study on ten multi-label data sets illustrates that our method is a powerful candidate for multi-label classification, compared with four state-of-the-art multi-label classification approaches. [Copyright &y& Elsevier]

Abstract: To address two problems, namely nonlinear problem and singularity problem, of linear discriminant analysis (LDA) approach in face recognition, this paper proposes a novel kernel machine-based rank-lifting regularized discriminant analysis (KRLRDA) method. A rank-lifting theorem is first proven using linear algebraic theory. Combining the rank-lifting strategy with three-to-one regularization technique, the complete regularized methodology is developed on the within-class scatter matrix. The proposed regularized scheme not only adjusts the projection directions but tunes their corresponding weights as well. Moreover, it is shown that the final regularized within-class scatter matrix approaches to the original one as the regularized parameter tends to zero. Two public available databases, namely FERET and CMU PIE face databases, are selected for evaluations. Compared with some existing kernel-based LDA methods, the proposed KRLRDA approach gives superior performance. [Copyright &y& Elsevier]

Abstract: This paper presents selective regeneration particle swarm optimization (SRPSO), a novel algorithm developed based on particle swarm optimization (PSO). It contains two new features, unbalanced parameter setting and particle regeneration operation. The unbalanced parameter setting enables fast convergence of the algorithm and the particle regeneration operation allows the search to escape from local optima and explore for better solutions. This algorithm is applied to data clustering problems for performance evaluation and a hybrid algorithm (KSRPSO) of K-means clustering method and SRPSO is developed. In the conducted numerical experiments, SRPSO and KSRPSO are compared to the original PSO algorithm, K-means, as well as, other methods proposed by other studies. The results demonstrate that SRPSO and KSRPSO are efficient, accurate, and robust methods for data clustering problems. [Copyright &y& Elsevier]

Abstract: The well-known problem of the longest common subsequence (LCS), of two strings of lengths and respectively, is -time solvable and is a classical distance measure for strings. Another well-studied string comparison measure is that of parameterized matching, where two equal-length strings are a parameterized match if there exists a bijection on the alphabets such that one string matches the other under the bijection. All works associated with parameterized pattern matching present polynomial time algorithms. There have been several attempts to accommodate parameterized matching along with other distance measures, as these turn out to be natural problems, e.g., Hamming distance, and a bounded version of edit-distance. Several algorithms have been proposed for these problems. In this paper we consider the longest common parameterized subsequence problem which combines the LCS measure with parameterized matching. We prove that the problem is -hard, and then show a couple of approximation algorithms for the problem. [Copyright &y& Elsevier]

Abstract: This paper studies online scheduling of unit length jobs on a parallel batching machine with the help of lookahead. The objective is to maximize the number of early jobs. Denote by the size of each batch with in the unbounded batching and in the bounded batching. In the lookahead model, at a time instant , an online algorithm can foresee all jobs that will arrive in the time segment . When , we show that a simple greedy online algorithm (independent of the value of ) has a best possible competitive ratio of , where is the number of jobs. This means that lookahead is useless when . When , we establish the upper bounds (for ) and (for ) of competitive ratios, and provide an online algorithm of competitive ratios (for ) and (for ). [Copyright &y& Elsevier]

Intersection-closed classes of concepts arise naturally in many contexts and have been intensively studied in computational learning theory. In this paper, we study intersection-closed classes that contain the concepts invariant under an operation satisfying a certain algebraic condition. We give a learning algorithm in the exact model with equivalence queries for such classes. This algorithm utilizes a novel encoding scheme, which we call a signature. [Copyright &y& Elsevier]