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.
6083596
Improved learning algorithms for mixture of experts in multiclass classification
Mixture of experts (ME) is a modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME architecture for adjusting the parameters and the iteratively reweighted least squares (IRLS) algorithm is used to perform maximization in the inner loop [Jordan, M.I., Jacobs, R.A. (1994). Hierarchical mixture of experts and the EM algorithm, Neural Computation, 6(2), 181-214]. However, it is reported in literature that the IRLS algorithm is of instability and the ME architecture trained by the EM algorithm, where IRLS algorithm is used in the inner loop, often produces the poor performance in multiclass classification. In this paper, the reason of this instability is explored. We find out that due to an implicitly imposed incorrect assumption on parameter independence in multiclass classification, an incomplete Hessian matrix is used in that IRLS algorithm. Based on this finding, we apply the Newton-Raphson method to the inner loop of the EM algorithm in the case of multiclass classification, where the exact Hessian matrix is adopted. To tackle the expensive computation of the Hessian matrix and its inverse, we propose an approximation to the Newton-Raphson algorithm based on a so-called generalized Bernoulli density. The Newton-Raphson algorithm and its approximation have been applied to synthetic data, benchmark, and real-world multiclass classification tasks. For comparison, the IRLS algorithm and a quasi-Newton algorithm called BFGS have also been applied to the same tasks. Simulation results have shown that the use of the proposed learning algorithms avoids the instability problem and makes the ME architecture produce good performance in multiclass classification. In particular, our approximation algorithm leads to fast learning. In addition, the limitation of our approximation algorithm is also empirically investigated in this paper.
14770227
Data smoothing regularization, multi-sets-learning, and problem solving strategies
First, we briefly introduce the basic idea of data smoothing regularization, which was firstly proposed by Xu [Brain-like computing and intelligent information systems (1997) 241] for parameter learning in a way similar to Tikhonov regularization but with an easy solution to the difficulty of determining an appropriate hyper-parameter. Also, the roles of this regularization are demonstrated on Gaussian-mixture via smoothed versions of the EM algorithm, the BYY model selection criterion, adaptive harmony algorithm as well as its related Rival penalized competitive learning. Second, these studies are extended to a mixture of reconstruction errors of Gaussian types, which provides a new probabilistic formulation for the multi-sets learning approach [Proc. IEEE ICNN94 I (1994) 315] that learns multiple objects in typical geometrical structures such as points, lines, hyperplanes, circles, ellipses, and templates of given shapes. Finally, insights are provided on three problem solving strategies, namely the competition-penalty adaptation based learning, the global evidence accumulation based selection, and the guess-test based decision, with a general problem solving paradigm suggested.
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.
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.
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.
7919733
Discovering direct and indirect matches for schema elements
Automating schema matching is challenging. Previous approaches to automating schema matching focus on computing direct element matches between two schemas. Schemas, however rarely match directly. Thus, to complete the task of schema matching, we must also compute indirect element matches. In this paper we present a framework for generating direct as well as many indirect element matches between a source schema and a target schema. Recognizing expected data values associated with schema elements and applying schema-structure heuristics are the key ideas to computing indirect matches. Experiments we have conducted over several real-world application domains show encouraging results, yielding over 90% precision and recall for both direct and indirect element matches.
2996848
Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data
MOTIVATION
DNA microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers.


RESULTS
We present a novel, simple method of integrating different microarray datasets to identify marker genes and apply the method to prostate cancer datasets. In this study, by applying a new statistical method, referred to as the top-scoring pair (TSP) classifier, we have identified a pair of robust marker genes (HPN and STAT6) by integrating microarray datasets from three different prostate cancer studies. Cross-platform validation shows that the TSP classifier built from the marker gene pair, which simply compares relative expression values, achieves high accuracy, sensitivity and specificity on independent datasets generated using various array platforms. Our findings suggest a new model for the discovery of marker genes from accumulated microarray data and demonstrate how the great wealth of microarray data can be exploited to increase the power of statistical analysis.


CONTACT
leixu@jhu.edu.