This paper introduces a model of multipleinstance learning applied to the prediction of aspect ratings or judgments of specific properties of an item from usercontributed texts such as product reviews. Each variable-length text is represented by several independent feature vectors; one word vector per sentence or paragraph. For learning from texts with known aspect ratings, the model performs m...

Summary: Robust conversion between microarray platforms is needed to leverage the wide variety of microarray expression studies that have been conducted to date. Currently available conversion methods rely on manufacturer annotations, which are often incomplete, or on direct alignment of probes from different platforms, which often fail to yield acceptable genewise correlation. Here, we describ...

This research aims to develop a tsunami vulnerability assessment model on land use and cover using information NDVI, NDWI, MDWI, MSAVI, NDBI extracted from sentinel 2 A ASTER satellite images. The optimization algorithms LASSO linear regression. validation test is MSE, ME, RMSE MAE which show that the regression has higher accuracy than LASSO. NDWI interpolation values are 0.00 - (-0.35) MNDWI ...

We design differentially private algorithms for statistical model selection. Given a data set and alarge, discrete collection of “models”, each of which is a family of probability distributions, the goal isto determine the model that best “fits” the data. This is a basic problem in many areas of statistics andmachine learning.We consider settings in which there is a well-defined...

We consider the problem of minimizing block-separable convex functions subject to linear constraints. While the Alternating Direction Method of Multipliers (ADMM) for two-block linear constraints has been intensively studied both theoretically and empirically, in spite of some preliminary work, effective generalizations of ADMM to multiple blocks is still unclear. In this paper, we propose a pa...

Due to the multiple loci control nature of complex phenotypes, there is great interest to test markers simultaneously instead of one by one. In this paper, we compare three model selection methods for genome wide association studies using simulations: the Stochastic Search Variable Selection (SSVS), the Least Absolute Shrinkage and Selection Operator (LASSO) and the Elastic Net. We also apply t...

In this paper, we propose an approach for learning regression models efficiently in an environment where multiple features and data-points are added incrementally in a multistep process. At each step, any finite number of features maybe added and hence, the setting is not amenable to low rank updates. We show that our approach is not only efficient and optimal for ordinary least squares, weight...

Using a multiplicative reparametrization, it is shown that a subclass of Lq penalties with q less than or equal to one can be expressed as sums of L2 penalties. It follows that the lasso and other norm-penalized regression estimates may be obtained using a very simple and intuitive alternating ridge regression algorithm. As compared to a similarly intuitive EM algorithm for Lq optimization, the...

The Lasso has been widely studied and used in many applications over the last decade. It has also been extended in various directions in particular to ensure asymptotic oracle properties through adaptive weights (Zou, 2006). Another direction has been to incorporate additional knowledge within the penalty to account for some structure among features. Among such strategies the Fused-Lasso (Tibsh...

Variable selection in genome-wide association studies can be a daunting task and statistically challenging because there are more variables than subjects. We propose an approach that uses principal-component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) to identify gene-gene interaction in genome-wide association studies. A PCA was used to first reduce the dimension...