11044770
A PCA approach for fast retrieval of structural patterns in attributed graphs
An attributed graph (AG) is a useful data structure for representing complex patterns in a wide range of applications such as computer vision, image database retrieval, and other knowledge representation tasks where similar or exact corresponding structural patterns must be found. Existing methods for attributed graph matching (AGM) often suffer from the combinatorial problem whereby the execution cost for finding an exact or similar match is exponentially related to the number of nodes the AG contains. The square matching error of two AGs subject to permutations is approximately relaxed to a square matching error of two AGs subject to orthogonal transformations. Hence, the principal component analysis (PCA) algorithm can be used for the fast computation of the approximate matching error, with a considerably reduced execution complexity. Experiments demonstrate that this method works well and is robust against noise and other simple types of transformations.
11164646
Strip line detection and thinning by RPCL-based local PCA
We solve the tasks of strip line detection and thinning in image processing and pattern recognition with the help of a statistical learning technique called rival penalized competitive learning based local principal component analysis. Due to its model selection and noise resistance ability, the technique is experimentally shown to outperform conventional Hough transform and thinning algorithms.
37879675
A new curve detection method: Randomized Hough transform (RHT)
Abstract A new method is proposed for curve detection. For a curve with n parameters, instead of transforming one pixel into a hypersurface of the n -D parameter space as the HT and its variants do, we randomly pick n pixels and map them into one point in the parameter space. In comparison with the HT and its variants, our new method has the advantages of small storage, high speed, infinite parameter space and arbitrarily high resolution. The preliminary experiments have shown that the new method is quite effective.
6958967
Dual multivariate auto-regressive modeling in state space for temporal signal separation
Many existing independent component analysis (ICA) approaches result in deteriorated performance in temporal source separation because they have not taken into consideration of the underlying temporal structure of sources. In this paper, we model temporal sources as a general multivariate auto-regressive (AR) process whereby an underlying multivariate AR process in observation space is obtained. In this dual AR modeling, the mixing process from temporal sources to observations is the same as the mixture from the nontemporal residuals of the source AR (SAR) process to that of the observation AR (OAR) process. We can therefore avoid the source temporal effects in performing ICA by learning the demixing system on the independently distributed OAR residuals rather than the time-correlated observations. Particularly, we implement this approach by modeling each source signal as a finite mixture of generalized autoregressive conditional heteroskedastic (GARCH) process. The adaptive algorithms are proposed to extract the OAR residuals appropriately online, together with learning the demixing system via a nontemporal ICA algorithm. The experiments have shown its superior performance on temporal source separation.
6364266
BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
The Bayesian Ying-Yang (BYY) harmony learning acts as a general statistical learning framework, featured by not only new regularization techniques for parameter learning but also a new mechanism that implements model selection either automatically during parameter learning or via a new class of model selection criteria used after parameter learning. In this paper, further advances on BYY harmony learning by considering modular inner representations are presented in three parts. One consists of results on unsupervisedmixture models, ranging from Gaussian mixture based Mean Square Error (MSE) clustering, elliptic clustering, subspace clustering to NonGaussian mixture based clustering not only with each cluster represented via either Bernoulli-Gaussian mixtures or independent real factor models, but also with independent component analysis implicitly made on each cluster. The second consists of results on supervised mixture-of-experts (ME) models, including Gaussian ME, Radial Basis Function nets, and Kernel regressions. The third consists of two strategies for extending the above structural mixtures into self-organized topological maps. All these advances are introduced with details on three issues, namely, (a) adaptive learning algorithms, especially elliptic, subspace, and structural rival penalized competitive learning algorithms, with model selection made automatically during learning; (b) model selection criteria for being used after parameter learning, and (c) how these learning algorithms and criteria are obtained from typical special cases of BYY harmony learning.
16709558
One-Bit-Matching Conjecture for Independent Component Analysis
The one-bit-matching conjecture for independent component analysis (ICA) could be understood from different perspectives but is basically stated as all the sources can be separated as long as there is a one-toone same-sign-correspondence between the kurtosis signs of all source probability density functions (pdf's) and the kurtosis signs of all model pdf's (Xu, Cheung, & Amari, 1998a). This conjecture has been widely believed in the ICA community and implicitly supported by many ICA studies, such as the Extended Infomax (Lee, Girolami, & Sejnowski, 1999) and the soft switching algorithm (Welling & Weber, 2001). However, there is no mathematical proof to confirm the conjecture theoretically. In this article, only skewness and kurtosis are considered, and such a mathematical proof is given under the assumption that the skewness of the model densities vanishes. Moreover, empirical experiments are demonstrated on the robustness of the conjecture as the vanishing skewness assumption breaks. As a by-product, we also show that the kurtosis maximization criterion (Moreau & Macchi, 1996) is actually a special case of the minimum mutual information criterion for ICA.
30598486
Regression testing for Web applications based on slicing
Web applications have rapid developing speed and changeable user demands, so the regression testing is much important. Since the changed demands result in different versions of Web applications, and the faults usually hiding in the adjusted contents, the regression testing must cover all the related pages. In order to carry through the regression testing quickly and effectively, we make the simplification with the method of slicing. Firstly, we analyze the possible changes in the Web applications and the influences produced by these changes, discussing in the direct-dependent and indirect-dependent way; next, we give the regression testing method based on slicing emphasized on the indirect-dependent among data, i.e., obtaining the dependent set of changed variables by forward and backward search method and generating the testing suits; conclusion remarks and future work are given at last.
39559674
A framework for Web applications testing
Web application testing is concerned with numerous and complicated testing objects, methods and processes. So a testing framework fitting for the properties of Web application is needed to guide and organize all the testing tasks. Based on the analysis for Web application characters and traditional software testing process, the process for Web application testing is modeled, which describes a series of testing flows such as the testing requirement analysis, test cases generation and selection, testing execution, and testing results analysis and measurement. Furthermore, the realization techniques are also investigated so as to integrate each testing step and implement the whole testing process harmoniously and effectively. Thus the framework is suitable for the Internet environment and can guide the Web application testing actively and availably.
32570723
Parallel algorithm for mining fuzzy association rules
The principle and steps of the algorithm for mining fuzzy association rules is studied, and the parallel algorithm for mining fuzzy association rules is presented. In this parallel mining algorithm, quantitative attributes are partitioned into several fuzzy sets by the parallel fuzzy c-means algorithm, and fuzzy sets are applied to soften the partition boundary of the attributes. Then, the parallel algorithm for mining Boolean association rules is improved to discover frequent fuzzy attributes. Last, the fuzzy association rules with at least fuzzy confidence are generated on all processors. The parallel mining algorithm is implemented on the distributed linked PC/workstation. The experiment results show that the parallel mining algorithm has fine scaleup, sizeup and speedup.
31040454
Determining optimum edible films for kiwifruits using an analytical hierarchy process
Fresh fruits undergo vigorous biological reaction after harvest and their respiration accelerates the natural loss of fruit tissue. Therefore, fruits tend to lose water at room temperature, change appearance, texture, and quality, and thus reduce their commercial value. Kiwifruits rot faster than other fruits due to their high ethylene concentration in atmosphere and their sensitivity to ethylene. Coating fruits with an edible film is an effective storage method at room temperature. In this paper, mathematical models of the permeability of edible films and the analytic hierarchy process model are used to determine the optimum edible film for preserving kiwifruits.
18082864
Performance analysis of an ingress switch in a JumpStart optical burst switching network
We consider an ingress optical burst switching (OBS) node employing the JumpStart signaling protocol. The switch serves a number of users, each connected to the switch with a fiber link that supports multiple wavelengths. Each wavelength is associated with a 3-state Markovian burst arrival process which permits short and long bursts to be modeled. We model the ingress switch as a closed multi-class non-product-form queueing network, which we analyze approximately by decomposition. Specifically, we develop new techniques to analyze the queueing network, first assuming a single class of customers, and subsequently multiple classes of customers. These analytical techniques have applications to general queueing networks beyond the one studied in this paper. We also develop computationally efficient approximate algorithms to analyze an ingress switch in the limiting case where the number of wavelengths is large. The algorithms have a good accuracy, and they provide insight into the effect of various system parameters on the performance of an ingress OBS switch.