In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the `0 pseudo norm is able to better induce sparsity than the commonly used `1 norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corres...

Modern molecular biotechnology generates a great deal of intermediate information, such as transcriptional and metabolic products in bridging DNA and complex traits. In genome-wide linkage analysis and genome-wide association study, regression analysis for large-scale correlated phenotypes is applied to map genes for those by-products that are regarded as quantitative traits. For a single trait...

We propose two classes of second-order optimization methods for solving the sparse inverse covariance estimation problem. The first approach, which we call the Newton-LASSO method, minimizes a piecewise quadratic model of the objective function at every iteration to generate a step. We employ the fast iterative shrinkage thresholding method (FISTA) to solve this subproblem. The second approach,...

The decision tree model has gained great popularity both in academia and industry due to its capability of learning highly non-linear decision boundaries, and at the same time, still preserving interpretability that usually translates into transparency of decision-making. However, it has been a longstanding challenge for learning robust decision tree models since the learning process is usually...

At cure temperatures below Tg the cure shrinkage contributes significantly to residual stress build-up. Residual stresses in a flip chip package The cure shrinkage model was implemented in FE code. In figures 2 and 3 the residual stress build-up for a 2D plane strain model of an adhesively bonded flip chip is shown. A schematic representation of the model is given in the inset of figure 3. For ...

In a recent paper van Houwelingen and le Cessie (1990) consider the shrinkage eeect as well as a shrinkage method. We investigate the shrinkage eeect in more detail and show that the proposed shrinkage method is tailored to remove the shrinkage eeect. This appears to be not a good advice to improve predictors. However, it can be useful in predicting the future behaviour of risk groups deened by...

This paper proposes new enhancement models to the methods of nonlinear anisotropic diffusion to greatly reduce speckle and preserve image features in medical ultrasound images. By incorporating local physical characteristics of the image, in this case scatterer density, in addition to the gradient, into existing tensorbased image diffusion methods, we were able to greatly improve the performanc...

We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings. We show an efficient learning algorithm based on stochastic proximal methods that is significantly faster than previous approaches, making it possible to perform hierarchical sparse coding on a corpus of billions of word tokens. Experime...

The paper presents a method for sparse matrix multiplication on a DSP processor. Its high efficiency is a consequence of the proposed pseudo-random data memory access and parallelism of the multifunctional instructions of a DSP. Sparse matrix multiplication is implemented as linear expanded DSP code automatically generated by specially designed program. The method is applied to predictive vecto...