Ye. Efficient methods for overlapping group lasso. NIPS, 2011.L. Yuan, J. Liu, and J. Ye, "Efficient methods for over- lapping group lasso," in Advances in NIPS, 2011, pp. 352-360.Yuan L., et al. Adv. Neural In
It is more general than current approaches and, as we show with numerical simulations, computationally more efficient than available first order methods which do not achieve the optimal rate. In particular, our method outperforms state of the art O(1/T) methods for overlapping Group Lasso and ...
2. For the human samples, 18604 gene expression data were provided. Methods Gene selection The basic idea of our gene signature extraction approach is to identify an overlapping among the most discriminant genes we found out by applying three different feature selection techniques: 1 Feature ...
We can see that iSeg, DNACopy and CGHSeg perform similarly well, with HMMseg and CGHFLasso performing a little worse while fastseg did not perform as well as the other methods. iSeg is also tested using a set of 10 longer simulated Fig. 2 One of the simulated profiles and its detected...
To make it feasible to apply our methods to biobank-based GWAS data, we further develop a novel distributed memory parallel computing algorithm utilizing MPI. First, large-scale GWAS data are divided into non-overlapping subgroups contain- ing SNPs in low LD within each subgroup and each MPI ...
least-squares methods. Among many regularized methods, the Lasso and the MCP are evaluated for inducing a sparse solution. In order to incorporate the shared genetic effects across traits for improving PA, we propose a cross-trait penalty which is a smooth function of pairwise genetic effects. ...
For analysis of each of the columns to understand the relationship between them Pearson's and LASSO's Coefficient is implemented. Traditional classification algorithms have an accuracy capped at 74% with MLP being the maximum 73.9%. The modern bagging and boosting methods combined with these ...
In particular, our method outperforms state of the art O(1/T) methods for overlapping Group Lasso and matches optimal O(1/T^2) methods for the Fused Lasso and tree structured Group Lasso.Andreas ArgyriouCharles A. MicchelliMassimiliano Pontil...
The proposed work addresses the limitations of previous methods. Our contribution in this study is as follows: 1. A most accurate and efficient end-to-end fully automated deep learning architecture is proposed for grading renal tumors from H &E stained kidney histopathology images. 2. This study...
In this sense, observers seemed to lack regularization, which is an umbrella term for all kinds of processes that prevent overfitting by introducing additional information, for example to use as few nonzero parameters as possible during fitting. Essentially, regularization methods improve generalization ...