7432960
Bayesian Self-Organization
Recent work by Becker and Hinton (Becker and Hinton, 1992) shows a promising mechanism, based on maximizing mutual information assuming spatial coherence, by which a system can self-organize itself to learn visual abilities such as binocular stereo. We introduce a more general criterion, based on Bayesian probability theory, and thereby demonstrate a connection to Bayesian theories of visual perception and to other organization principles for early vision (Atick and Redlich, 1990). Methods for implementation using variants of stochastic learning are described and, for the special case of linear filtering, we derive an analytic expression for the output.
15052539
A Further Result on the ICA One-Bit-Matching Conjecture
The one-bit-matching conjecture for independent component analysis (ICA) has been widely believed in the ICA community. Theoretically, it has been proved that under the assumption of zero skewness for the model probability density functions, the global maximum of a cost function derived from the typical objective function on the ICA problem with the one-bit-matching condition corresponds to a feasible solution of the ICA problem. In this note, we further prove that all the local maximums of the cost function correspond to the feasible solutions of the ICA problem in the two-source case under the same assumption. That is, as long as the one-bit-matching condition is satisfied, the two-source ICA problem can be successfully solved using any local descent algorithm of the typical objective function with the assumption of zero skewness for all the model probability density functions.
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.
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.
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.
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.
8691766
Media Streaming via TFRC: An Analytical Study of the Impact of TFRC on User-Perceived Media Quality
TCP-Friendly Rate Control (TFRC) is being adopted in Internet standards for congestion control of various streaming media applications. In this paper, we consider the transmission of pre-recorded media from a server to a client by using TFRC, and analytically study the impact of TFRC on user-perceived media quality, which is roughly measured by calculating the rebuffering probability. A rebuffering probability is defined to be the probability that the total duration of all rebuffering events experienced by a user is longer than a certain threshold. Two approaches are presented to help an application determine an appropriate initial buffering delay and media playback rate in order to achieve a certain rebuffering probability under a given network condition. First, we derive a closed- form expression to approximate the average TFRC sending rate, which could be used as the maximum allowed playback rate of a media stream. Second, we develop a queueing model for a TFRC client buffer with the traffic described by a Markov-Renewal- Modulated Deterministic Process (MRMDP), and present an iterative method to calculate the rebuffering probability.
8227025
A Browser Compatibility Testing Method Based on Combinatorial Testing
For ensuring the displaying effects of Web applications, it is important to perform compatibility testing for browsers under the different configurations and it is a hard task to test all. So this paper focused on the improvements for browser compatibility testing. Firstly, we introduced the related work. Then we analysed the functional speciality of the current popular browsers, and provided the preconditions to simplify problems. Next we gave an instance and brought forward the single factor covering method and the pair-wise covering design to gain testing suits. Finally, we introduced the assistant tools we have developed and the future work.
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.
31813405
Website evolution based on statistic data
Maintenance and evolution are critical for website since the requirements often change, the developing cycle is short, while the life cycle is long. In order to make progress in this area, we focus on the users' responses and attitudes. So we begin our work at the log files on the side of website server, gathering the users' visiting information and the server's responses. Thus we obtain our weighted structure model. Then, we analyze and discuss such information to identify the key pages, the predominate pages and the users' visiting patterns. And we can improve the testing efficiency based on these knowledge. All these will help to improve the structure of the site, fulfill the functionality of the site, and enhance users' visiting efficiency.
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.