1378528
An optimization criterion for generalized discriminant analysis on undersampled problems
An optimization criterion is presented for discriminant analysis. The criterion extends the optimization criteria of the classical Linear Discriminant Analysis (LDA) through the use of the pseudoinverse when the scatter matrices are singular. It is applicable regardless of the relative sizes of the data dimension and sample size, overcoming a limitation of classical LDA. The optimization problem can be solved analytically by applying the Generalized Singular Value Decomposition (GSVD) technique. The pseudoinverse has been suggested and used for undersampled problems in the past, where the data dimension exceeds the number of data points. The criterion proposed in this paper provides a theoretical justification for this procedure. An approximation algorithm for the GSVD-based approach is also presented. It reduces the computational complexity by finding subclusters of each cluster and uses their centroids to capture the structure of each cluster. This reduced problem yields much smaller matrices to which the GSVD can be applied efficiently. Experiments on text data, with up to 7,000 dimensions, show that the approximation algorithm produces results that are close to those produced by the exact algorithm.
8864585
Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis
In this paper, we present a new approach for fingerprint classification based on discrete Fourier transform (DFT) and nonlinear discriminant analysis. Utilizing the DFT and directional filters, a reliable and efficient directional image is constructed from each fingerprint image, and then nonlinear discriminant analysis is applied to the constructed directional images, reducing the dimension dramatically and extracting the discriminant features. The proposed method explores the capability of DFT and directional filtering in dealing with low-quality images and the effectiveness of nonlinear feature extraction method in fingerprint classification. Experimental results demonstrates competitive performance compared with other published results.
1459623
A C-based RTL design verification methodology for complex microprocessor
As the complexity of high-performance microprocessor increases,functional verification becomes more and more difficultand RTL simulation emerges as the bottleneck of thedesign cycle.In this paper, we suggest C language-based designand verification methodology to enhance the simulationspeed instead of the conventional HDL-based methodologies.RTL C model (StreC) describes the cycle-based behaviors ofsynchronous circuits and is followed by model refining andoptimization using LifeTime Analyzer (LTA) and Cleaner.The simulation speed of cycle-based C model makes it possibleto test the RTL design with the "real-world" applicationprograms in the order-of-magnitude faster speed thanthe commercial event-driven simulators.Using the proposedfunctional verification methodology, HK486, an intel 80486 - compatiblemicroprocessor was successfully designed and verified.
45146942
Two families of graphs that are not CCE-orientable
16796356
Some variants of minimum disparity estimation
Abstract This paper proposes several variants of disparity-based inference (Ann. Statist. 22 (1994) 1081–1114). We introduce these modifications and explain the motivation behind them. Several of these estimators and tests have attractive efficiency and robustness properties. An extensive numerical and graphical investigation is presented to substantiate the theory developed and demonstrate the small sample properties of these methods. An empty cell penalty is found to greatly enhance the performance of some of these methods.