Parsimonious Kernel Fisher Discrimination

By applying recent results in optimization transfer, a new algorithm for kernel Fisher Discriminant Analysis is provided that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The algorithm is simple, easily programmed and is shown to perform as well as or better than a number of leading machine learning algorithms on a substantial benchmark. It is then applied to a set of extreme small-sample-size problems in virtual screening where it is found to be less accurate than a currently leading approach but is still comparable in a number of cases

Fast weighted summation of erfc functions

Abstract: Direct computation of the weighted sum of N complementary error functions at M points scales as O(MN). We present a O(M + N) $\epsilon$-exact approximation algorithm to compute the same

Beyond brain reading: randomized sparsity and clustering to simultaneously predict and identify

International audienceThe prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. From a statistical standpoint, this challenge is a supervised learning task. The ability to predict cognitive states from new data gives a model selection criterion: prediction accu- racy. While a good prediction score implies that some of the voxels used by the classifier are relevant, one cannot state that these voxels form the brain regions involved in the cognitive task. The best predictive model may have selected by chance non-informative regions, and neglected rele- vant regions that provide duplicate information. In this contribution, we address the support identification problem. The proposed approach relies on randomization techniques which have been proved to be consistent for support recovery. To account for the spatial correlations between voxels, our approach makes use of a spatially constrained hierarchical clustering algorithm. Results are provided on simulations and a visual experiment

A Simple Iterative Algorithm for Parsimonious Binary Kernel Fisher Discrimination

By applying recent results in optimization theory variously known as optimization transfer or majorize/minimize algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimization that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks

Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

Penalized regression is an attractive framework for variable selection problems. Often, variables possess a grouping structure, and the relevant selection problem is that of selecting groups, not individual variables. The group lasso has been proposed as a way of extending the ideas of the lasso to the problem of group selection. Nonconvex penalties such as SCAD and MCP have been proposed and shown to have several advantages over the lasso; these penalties may also be extended to the group selection problem, giving rise to group SCAD and group MCP methods. Here, we describe algorithms for fitting these models stably and efficiently. In addition, we present simulation results and real data examples comparing and contrasting the statistical properties of these methods

Unsupervised Classification of SAR Images using Hierarchical Agglomeration and EM

We implement an unsupervised classification algorithm for high resolution Synthetic Aperture Radar (SAR) images. The foundation of algorithm is based on Classification Expectation-Maximization (CEM). To get rid of two drawbacks of EM type algorithms, namely the initialization and the model order selection, we combine the CEM algorithm with the hierarchical agglomeration strategy and a model order selection criterion called Integrated Completed Likelihood (ICL). We exploit amplitude statistics in a Finite Mixture Model (FMM), and a Multinomial Logistic (MnL) latent class label model for a mixture density to obtain spatially smooth class segments. We test our algorithm on TerraSAR-X data

E4orf1: A Novel Ligand That Improves Glucose Disposal in Cell Culture

Reducing dietary fat intake and excess adiposity, the cornerstones of behavioral treatment of insulin resistance(IR), are marginally successful over the long term. Ad36, a human adenovirus, offers a template to improve IR, independent of dietary fat intake or adiposity. Ad36 increases cellular glucose uptake via a Ras-mediated activation of phosphatidyl inositol 3-kinase(PI3K), and improves hyperglycemia in mice, despite a high-fat diet and without reducing adiposity. Ex-vivo studies suggest that Ad36 improves hyperglycemia in mice by increasing glucose uptake by adipose tissue and skeletal muscle, and by reducing hepatic glucose output. It is impractical to use Ad36 for therapeutic action. Instead, we investigated if the E4orf1 protein of Ad36, mediates its anti-hyperglycemic action. Such a candidate protein may offer an attractive template for therapeutic development. Experiment-1 determined that Ad36 ‘requires’ E4orf1 protein to up-regulate cellular glucose uptake. Ad36 significantly increased glucose uptake in 3T3-L1 preadipocytes, which was abrogated by knocking down E4orf1 with siRNA. Experiment-2 identified E4orf1 as ‘sufficient’ to up-regulate glucose uptake. 3T3-L1 cells that inducibly express E4orf1, increased glucose uptake in an induction-dependent manner, compared to null vector control cells. E4orf1 up-regulated PI3K pathway and increased abundance of Ras–the obligatory molecule in Ad36-induced glucose uptake. Experiment-3: Signaling studies of cells transiently transfected with E4orf1 or a null vector, revealed that E4orf1 may activate Ras/PI3K pathway by binding to Drosophila discs-large(Dlg1) protein. E4orf1 activated total Ras and, particularly the H-Ras isoform. By mutating the PDZ domain binding motif(PBM) of E4orf1, Experiment-4 showed that E4orf1 requires its PBM to increase Ras activation or glucose uptake. Experiment-5: In-vitro, a transient transfection by E4orf1 significantly increased glucose uptake in preadipocytes, adipocytes, or myoblasts, and reduced glucose output by hepatocytes. Thus, the highly attractive anti-hyperglycemic effect of Ad36 is mirrored by E4orf1 protein, which may offer a novel ligand to develop anti-hyperglycemic drugs

Evidential Clustering: A Review

International audienceIn evidential clustering, uncertainty about the assignment of objects to clusters is represented by Dempster-Shafer mass functions. The resulting clustering structure, called a credal partition, is shown to be more general than hard, fuzzy, possibilistic and rough partitions, which are recovered as special cases. Three algorithms to generate a credal partition are reviewed. Each of these algorithms is shown to implement a decision-directed clustering strategy. Their relative merits are discussed