Sparsifying the Fisher Linear Discriminant by Rotation

Many high dimensional classification techniques have been proposed in the literature based on sparse linear discriminant analysis (LDA). To efficiently use them, sparsity of linear classifiers is a prerequisite. However, this might not be readily available in many applications, and rotations of data are required to create the needed sparsity. In this paper, we propose a family of rotations to create the required sparsity. The basic idea is to use the principal components of the sample covariance matrix of the pooled samples and its variants to rotate the data first and to then apply an existing high dimensional classifier. This rotate-and-solve procedure can be combined with any existing classifiers, and is robust against the sparsity level of the true model. We show that these rotations do create the sparsity needed for high dimensional classifications and provide theoretical understanding why such a rotation works empirically. The effectiveness of the proposed method is demonstrated by a number of simulated and real data examples, and the improvements of our method over some popular high dimensional classification rules are clearly shown.Comment: 30 pages and 9 figures. This paper has been accepted by Journal of the Royal Statistical Society: Series B (Statistical Methodology). The first two versions of this paper were uploaded to Bin Dong's web site under the title "A Rotate-and-Solve Procedure for Classification" in 2013 May and 2014 January. This version may be slightly different from the published versio

Variance Estimation Using Refitted Cross-validation in Ultrahigh Dimensional Regression

Variance estimation is a fundamental problem in statistical modeling. In ultrahigh dimensional linear regressions where the dimensionality is much larger than sample size, traditional variance estimation techniques are not applicable. Recent advances on variable selection in ultrahigh dimensional linear regressions make this problem accessible. One of the major problems in ultrahigh dimensional regression is the high spurious correlation between the unobserved realized noise and some of the predictors. As a result, the realized noises are actually predicted when extra irrelevant variables are selected, leading to serious underestimate of the noise level. In this paper, we propose a two-stage refitted procedure via a data splitting technique, called refitted cross-validation (RCV), to attenuate the influence of irrelevant variables with high spurious correlations. Our asymptotic results show that the resulting procedure performs as well as the oracle estimator, which knows in advance the mean regression function. The simulation studies lend further support to our theoretical claims. The naive two-stage estimator which fits the selected variables in the first stage and the plug-in one stage estimators using LASSO and SCAD are also studied and compared. Their performances can be improved by the proposed RCV method

Projective Truncation Approximation for Equations of Motion of Two-Time Green's Functions

In the equation of motion approach to the two-time Green's functions, conventional Tyablikov-type truncation of the chain of equations is rather arbitrary and apt to violate the analytical structure of Green's functions. Here, we propose a practical way to truncate the equations of motion using operator projection. The partial projection approximation is introduced to evaluate the Liouville matrix. It guarantees the causality of Green's functions, fulfills the time translation invariance and the particle-hole symmetry, and is easy to implement in a computer. To benchmark this method, we study the Anderson impurity model using the operator basis at the level of Lacroix approximation. Improvement over conventional Lacroix approximation is observed. The distribution of Kondo screening in the energy space is studied using this method.Comment: 14 pages, 8 figures, published versio

A molecular dynamics investigation of the mechanical properties of graphene nanochain

In this paper, we investigate, by molecular dynamics simulations, the mechanical properties of a new carbon nanostructure, termed graphene nanochain, constructed by sewing up pristine or twisted graphene nanoribbons (GNRs) and interlocking the obtained nanorings. The obtained tensile strength of defect-free nanochain is a little lower than that of pristine GNRs and the fracture point is earlier than that of the GNRs. The effects of length, width and twist angle of the constituent GNRs on the mechanical performance are analyzed. Furthermore, defect effect is investigated and in some high defect coverage cases, an interesting mechanical strengthening-like behavior is observed. This structure supports the concept of long-cable manufacturing and advanced material design can be achieved by integration of nanochain with other nanocomposites. The technology used to construct the nanochain is experimentally feasible, inspired by the recent demonstrations of atomically precise fabrications of GNRs with complex structures [Phys. Rev. Lett,2009,\textbf{102},205501; Nano Lett., 2010, \textbf{10},4328; Nature,2010,\textbf{466},470]Comment: 26page