An efficient algorithm for incrementally mining frequent closed itemsets

The purpose of mining frequent itemsets is to identify the items in groups that always appear together and exceed the user-specified threshold of a transaction database. However, numerous frequent itemsets may exist in a transaction database, hindering decision making. Recently, the mining of frequent closed itemsets has become a major research issue because sets of frequent closed itemsets are condensed yet complete representations of frequent itemsets. Therefore, all frequent itemsets can be derived from a group of frequent closed itemsets. Nonetheless, the number of transactions in a transaction database can increase rapidly in a short time period, and a number of the transactions may be outdated. Thus, frequent closed itemsets may be changed with the addition of new transactions or the deletion of old transactions from the transaction database. Updating previously closed itemsets when transactions are added or removed from the transaction database is challenging. This study proposes an efficient algorithm for incrementally mining frequent closed itemsets without scanning the original database. The proposed algorithm updates closed itemsets by performing several operations on the previously closed itemsets and added/deleted transactions without searching the previously closed itemsets. The experimental results show that the proposed algorithm significantly outperforms previous methods, which require a substantial length of time to search previously closed itemsets.     

Stabilizing bit-rates in networked control systems with decentralized event-triggered communication

In a decentralized event-triggered networked control system (NCS), an agent samples and transmits its local state information to the controller when some local event occurs. Such event-triggered NCSs were expected to be more efficient than traditional periodically sampled system in terms of communication channel usage. This paper studies the stability of decentralized event-triggered NCS in the presence of quantization and delays. We point out some potential issues in decentralized event-triggered design and propose an alternative decentralized event with a linear-affine threshold, which avoids infinitely fast data transmission. Conditions on quantizer and communication channel are derived, which, when satisfied, can guarantee stability of the resulting NCS. Based on these conditions, finite stabilizing bit-rates are provided.     

An efficient KPCA algorithm based on feature correlation evaluation

Classic kernel principal component analysis (KPCA) is less computationally efficient when extracting features from large data sets. In this paper, we propose an algorithm, that is, efficient KPCA (EKPCA), that enhances the computational efficiency of KPCA by using a linear combination of a small portion of training samples, referred to as basic patterns, to approximately express the KPCA feature extractor, that is, the eigenvector of the covariance matrix in the feature extraction. We show that the feature correlation (i.e., the correlation between different feature components) can be evaluated by the cosine distance between the kernel vectors, which are the column vectors in the kernel matrix. The proposed algorithm can be easily implemented. It first uses feature correlation evaluation to determine the basic patterns and then uses these to reconstruct the KPCA model, perform feature extraction, and classify the test samples. Since there are usually many fewer basic patterns than training samples, EKPCA feature extraction is much more computationally efficient than that of KPCA. Experimental results on several benchmark data sets show that EKPCA is much faster than KPCA while achieving similar classification performance.     

Coordinated formation pattern control of multiple marine surface vehicles with model uncertainty and time-varying ocean currents

This paper presents a data-driven modeling and optimization method for cavity filters, according to a limited amount of measurement data. In the method, a model that reveals the effect of mechanical structure on electrical performance of cavity filters is firstly developed by an improved algorithm, which can increase the modeling accuracy of small data set by incorporating multi-kernel and prior knowledge into the framework of linear programming support vector regression. Then, an approach to optimize the structure of cavity filters is formulated by using the developed data-based model, and the obtained results can assist the fabrication of the same filter in the future. Some experiments from a synthetic example and a practical application of cavity filter have been carried out, and the experimental results confirm the effectiveness of the method. The model is particularly suited to a computer-aided manufacturing of volume-producing filters, and the proposed algorithm shows great potential in some applications where the experimental data are very few and the prior knowledge is available.     

Bifurcation analysis of a two-dimensional simplified Hodgkin–Huxley model exposed to external electric fields

In this paper, the dynamical behaviors of a two-dimensional simplified Hodgkin–Huxley (H–H) model exposed to external electric fields are investigated through qualitative analysis and numerical simulation. A necessary and sufficient condition is proposed for the existence of the Hopf bifurcation. Saddle-node bifurcations and canards of the simplified model with the coefficients of different linear forms are also discussed. Finally, the bifurcation curves with the coefficients of different linear forms are shown. The numerical results demonstrate that some linear forms can retain the bifurcation characteristics of the original model, which is of great use to simplify the H–H model for the real-world applications.     

Collaborative representation with reduced residual for face recognition

Collaboration representation-based classification (CRC) was proposed as an alternative approach to the sparse representation method with similar efficiency. The CRC is essentially a competition scheme for the training samples to compete with each other in representing the test sample, and the training class with the minimum representation residual from the test sample wins the competition in the classification. However, the representation error is usually calculated based on the Euclidean distance between a test sample and the weighted sum of all the same-class samples. This paper exploits alternative methods of calculating the representation error in the CRC methods to reduce the representation residual in a more optimal way, so that the sample classes compete with each other in a closer range to represent the test sample. A large number of face recognition experiments on three face image databases show that the CRC methods with optimized presentation residual achieve better performance than the original CRC, and the maximum improvement in classification accuracy is up to 12 %.     

Stability-based preference selection in affinity propagation

Recently, as one of the most popular exemplar-based clustering algorithms, affinity propagation has attracted a great amount of attention in various fields. The advantages of affinity propagation include the efficiency, insensitivity to cluster initialization and capability of finding clusters with less error. However, one shortcoming of the affinity propagation algorithm is that, the clustering results generated by affinity propagation strongly depend on the selection of exemplar preferences, which is a challenging model selection task. To tackle this problem, this paper investigates the clustering stability of affinity propagation for automatically selecting appropriate exemplar preferences. The basic idea is to define a novel stability measure for affinity propagation, based on which we can select exemplar preferences that generate the most stable clustering results. Consequently, the proposed approach is termed stability-based affinity propagation (SAP). Experimental results conducted on extensive real-world datasets have validated the effectiveness of the proposed SAP algorithm.     

Memristor-based chaotic neural networks for associative memory

In chaotic neural networks, the rich dynamic behaviors are generated from the contributions of spatio-temporal summation, continuous output function, and refractoriness. However, a large number of spatio-temporal summations in turn make the physical implementation of a chaotic neural network impractical. This paper proposes and investigates a memristor-based chaotic neural network model, which adequately utilizes the memristor with unique memory ability to realize the spatio-temporal summations in a simple way. Furthermore, the associative memory capabilities of the proposed memristor-based chaotic neural network have been demonstrated by conventional methods, including separation of superimposed pattern, many-to-many associations, and successive learning. Thanks to the nanometer scale size and automatic memory ability of the memristors, the proposed scheme is expected to greatly simplify the structure of chaotic neural network and promote the hardware implementation of chaotic neural networks.     

Learning high-dimensional correspondence via manifold learning and local approximation

The recent years have witnessed a surge of interests of learning high-dimensional correspondence, which is important for both machine learning and neural computation community. Manifold learning–based researches have been considered as one of the most promising directions. In this paper, by analyzing traditional methods, we summarized a new framework for high-dimensional correspondence learning. Within this framework, we also presented a new approach, Local Approximation Maximum Variance Unfolding. Compared with other machine learning–based methods, it could achieve higher accuracy. Besides, we also introduce how to use the proposed framework and methods in a concrete application, cross-system personalization (CSP). Promising experimental results on image alignment and CSP applications are proposed for demonstration.