Xiaoyan Wei

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With the increase in size and complexity of spatiotemporal data, traditional methods for performing statistical analysis are insufficient for meeting real-time requirements for mining information from Big Data, due to both dataand computing-intensive factors. To solve the Big Data challenges in geostatistics and to support decision-making, a high(More)
Linear discriminant analysis(LDA) is one of the most popular methods for feature extraction and dimensionality reduction,but it may encounter the so called small sample size(SSS) problem when applied to high dimensional data analysis such as face recognition.Many two-stage methods were proposed to solve this problem such as Fisherfaces,Direct LDA and Null(More)
and Applied Analysis 3 The kernel-based greedy algorithm can be summarized as below. Let t be a stopping time and let β be a positive constant. Set f̂0 β 0. And then for τ 1, 2, . . . , t, define ĥτ , α̂τ , β̂τ argmin h∈Ĥ,0≤α≤1,0≤β′≤β Ez ( 1 − α f̂ τ−1 β αβ′h ) , f̂ τ β 1 − α̂τ f̂ τ−1 β α̂τ β̂τ ĥτ . 1.6 Different from the regularized algorithms in 6, 12,(More)
Kernel methods play important roles in machine learning algorithms such as support vector machines. However, how to construct a suitable kernel remains difficult. Recently Ye et al proposed a new kind of kernel function named Chebyshev kernel based on orthogonal Chebyshev polynomials. But in fact the features of the nonlinear mapping determined by Chebyshev(More)
Semi-supervised ranking is a newly developed machine learning problem. In this paper, based on the graph constructed on both labeled and unlabeled data points, we propose a novel semi-supervised ranking algorithm in the transductive setting via a semi-supervised regression model. We also derive the solution in an explicit form for this model. Experiments on(More)
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