17900866
Robust extraction of specific signals with temporal structure
There is a trend to develop blind or semi-blind source extraction algorithms based on second-order statistics, due to its low computation load and fast processing speed. An important and primary work is done by Barros and Cichocki, who propose an extraction algorithm based on a time delay. The algorithm is simple and fast, but its performance is not satisfying. The paper extends their work and proposes a robust algorithm based on eigenvalue decomposition of several delayed covariance matrices. It is faster and has better performance, which is confirmed by theoretical analysis and computer simulations.
35739145
A Flexible Algorithm for Extracting Periodic Signals
In this paper, we propose a flexible two-stage algorithm for extracting desired periodic signals. In the first stage, if the period and phase information of the desired signal is available (or can be estimated), a minimum mean square error approach is used to coarsely recover the desired source signal. If only the period information is available (or can be estimated), a robust correlation based method is proposed to achieve the same goal. The second stage uses a higher-order statistics based Newton-like algorithm, derived from a constrained maximum likelihood criteria, to process the extracted noisy signal as cleanly as possible. A parameterized nonlinearity is adopted in this stage, adapted according to the estimated statistics of the desired signal. Compared with many existing extraction algorithms, the proposed algorithm has better performance, which is confirmed by simulations.
8318108
Weakly-supervised relation classification for information extraction
This paper approaches the relation classification problem in information extraction framework with bootstrapping on top of Support Vector Machines. A new bootstrapping algorithm is proposed and empirically evaluated on the ACE corpus. We show that the supervised SVM classifier using various lexical and syntactic features can achieve promising classification accuracy. More importantly, the proposed <i>BootProject</i> algorithm based on random feature projection can significantly reduce the need for labeled training data with only limited sacrifice of performance.
12552052
Fixed-slope universal lossy data compression
Corresponding to any lossless codeword length function l, three universal lossy data compression schemes are presented: one is with a fixed rate, another is with a fixed distortion, and a third is with a fixed slope. The former two universal lossy data compression schemes are the generalization of Yang-Kieffer's (see ibid., vol.42, no.1, p.239-45, 1995) results to the general case of any lossless codeword length function l, whereas the third is new. In the case of fixed-slope /spl lambda/>0, our universal lossy data compression scheme works as follows: for any source sequence x/sup n/ of length n, the encoder first searches for a reproduction sequence y/sup n/ of length n which minimizes a cost function n/sup -1/l(y/sup n/)+/spl lambda//spl rho//sub n/(x/sup n/, y/sup n/) over all reproduction sequences of length n, and then encodes x/sup n/ into the binary codeword of length l(y/sup n/) associated with y/sup n/ via the lossless codeword length function l, where /spl rho//sub n/(x/sup n/, y/sup n/) is the distortion per sample between x/sup n/ and y/sup n/. Under some mild assumptions on the lossless codeword length function l, it is shown that when this fixed-slope data compression scheme is applied to encode a stationary, ergodic source, the resulting encoding rate per sample and the distortion per sample converge with probability one to R/sub /spl lambda// and D/sub /spl lambda//, respectively, where (D/sub /spl lambda//, R/sub /spl lambda//) is the point on the rate distortion curve at which the slope of the rate distortion function is -/spl lambda/. This result holds particularly for the arithmetic codeword length function and Lempel-Ziv codeword length function. The main advantage of this fixed-slope universal lossy data compression scheme over the fixed-rate (fixed-distortion) universal lossy data compression scheme lies in the fact that it converts the encoding problem to a search problem through a trellis and then permits one to use some sequential search algorithms to implement it. Simulation results show that this fixed-slope universal lossy data compression scheme, combined with a suitable search algorithm, is promising.
6709890
An agent-based hybrid framework for database mining
While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques.
34732275
Signal extrapolation based on wavelet transform
The Papoulis-Gerchberg (PG) algorithm is used for band-limited signal extrapolation. The PG algorithm is generalized to signals in wavelet subspaces. The generalized PG algorithm is considered for both continuous and discrete time signals. Its convergence is investigated. Numerical examples show its performance.<<ETX>>
1184325
Profiling internet backbone traffic: behavior models and applications
Recent spates of cyber-attacks and frequent emergence of applications affecting Internet traffic dynamics have made it imperative to develop effective techniques that can extract, and make sense of, significant communication patterns from Internet traffic data for use in network operations and security management. In this paper, we present a general methodology for building comprehensive behavior profiles of Internet backbone traffic in terms of communication patterns of end-hosts and services. Relying on data mining and information-theoretic techniques, the methodology consists of significant cluster extraction, automatic behavior classification and structural modeling for in-depth interpretive analyses. We validate the methodology using data sets from the core of the Internet. The results demonstrate that it indeed can identify common traffic profiles as well as anomalous behavior patterns that are of interest to network operators and security analysts.