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Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such models, but usually involves a computationally heavy combinatorial search. Lasso (Tibshirani, 1996) is now being used as a computationally feasible alternative to model selection.(More)
Recently much attention has been devoted to model selection through regularization methods in regression and classification where features are selected by use of a penalty function (e.g. Lasso in Tibshirani, 1996). While the resulting sparsity leads to more interpretable models, one may want to further incorporate natural groupings or hierarchical(More)
Extracting useful information from high-dimensional data is an important focus of today's statistical research and practice. Penalized loss function minimization has been shown to be effective for this task both theoretically and empirically. With the virtues of both regularization and sparsity, the L1-penalized squared error minimization method Lasso has(More)
The purpose of this study is to summarize our experience in managing patients with an atypical or malignant meningioma at our institution, with a specific focus on determining the prognostic factors for treatment outcome. We reviewed the records of 126 patients with atypical or malignant meningiomas from January 2001 to August 2011. Data collected included(More)
Developed for multimedia and game applications, as well as other numerically intensive workloads, the CELL processor provides support both for highly parallel codes, which have high computation and memory requirements, and for scalar codes, which require fast response time and a full-featured programming environment. This first generation CELL processor(More)
In MANETs, network may consist of devices with multiple characteristics in terms of transmission power, energy, capacity etc. Especially in MANETs, network may consist of devices with multiple, nodes are likely to transmit at different power levels, thereby causing conversation links varying. This causes link asymmetry problem. The link asymmetry problem(More)
Many patients with traumatic spinal cord injury (SCI) report pain that persists indefinitely and is resistant to available therapeutic approaches. We recently showed that microglia become activated after experimental SCI and dynamically maintain hyperresponsiveness of spinal cord nociceptive neurons and pain-related behaviors. Mechanisms of signaling(More)
We propose an automatic approach to generate street-side 3D photo-realistic models from images captured along the streets at ground level. We first develop a multi-view semantic segmentation method that recognizes and segments each image at pixel level into semantically meaningful areas, each labeled with a specific object class, such as building, sky,(More)
Given n observations of a p-dimensional random vector, the covariance matrix and its inverse (precision matrix) are needed in a wide range of applications. Sample covariance (e.g. its eigenstructure) can misbehave when p is comparable to the sample size n. Regularization is often used to mitigate the problem. In this paper, we proposed an 1 penalized(More)
In this paper, we propose the Boosted Lasso (BLasso) algorithm that is able to produce an approximation to the complete regularization path for general Lasso problems. BLasso is derived as a coordinate descent method with a fixed small step size applied to the general Lasso loss function (L1 penalized convex loss). It consists of both a forward step and a(More)