5541868
Nonlinear feature extraction based on centroids and kernel functions
A nonlinear feature extraction method is presented which can reduce the data dimension down to the number of classes, providing dramatic savings in computational costs. The dimension reducing nonlinear transformation is obtained by implicitly mapping the input data into a feature space using a kernel function, and then finding a linear mapping based on an orthonormal basis of centroids in the feature space that maximally separates the between-class relationship. The experimental results demonstrate that our method is capable of extracting nonlinear features effectively so that competitive performance of classification can be obtained with linear classifiers in the dimension reduced space.
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.
36826895
Parametric inference of incomplete data with competing risks among several groups
We develop parametric inferential methods for the competing risks problem where data arise due to multiple causes of failure in several groups with censoring and possibly missing causes. We provide the general likelihood method and the closed-form maximum-likelihood estimators for the exponential model. Parametric tests are given for comparing different causes and groups. An extensive numerical and graphical investigation is presented to substantiate the proposed methods. A real-data example is illustrated.
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.
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.
22230565
Parameter estimation of incomplete data in competing risks using the EM algorithm
Consider a system which is made up of multiple components connected in a series. In this case, the failure of the whole system is caused by the earliest failure of any of the components, which is commonly referred to as competing risks. In certain situations, it is observed that the determination of the cause of failure may be expensive, or may be very difficult to observe due to the lack of appropriate diagnostics. Therefore, it might be the case that the failure time is observed, but its corresponding cause of failure is not fully investigated. This is known as masking. Moreover, this competing risks problem is further complicated due to possible censoring. In practice, censoring is very common because of time and cost considerations on experiments. In this paper, we deal with parameter estimation of the incomplete lifetime data in competing risks using the EM algorithm, where incompleteness arises due to censoring and masking. Several studies have been carried out, but parameter estimation for incomplete data has mainly focused on exponential models. We provide the general likelihood method, and the parameter estimation of a variety of models including exponential, s-normal, and lognormal models. This method can be easily implemented to find the MLE of other models. Exponential and lognormal examples are illustrated with parameter estimation, and a graphical technique for checking model validity.
17330280
New cumulative damage models for failure using stochastic processes as initial damage
Based on a generalized cumulative damage approach with a stochastic process describing initial damage for a material specimen, a broad class of statistical models for material strength is developed. Plausible choices of stochastic processes for the initial damage include Brownian motion, geometric Brownian motion, and the gamma process; and additive & multiplicative cumulative damage functions are considered. The resulting general statistical model gives an accelerated test form of the inverse Gaussian distribution, special cases of which include some existing models in addition to several new models. Model parameterizations & estimation by maximum likelihood from accelerated test data are discussed, and the applicability of the general model is illustrated for three sets of strength data. The proposed models are compared with the power-law Weibull model, and the inverse Gaussian generalized linear models.