We develop a novel online learning algorithm for the group lasso in order to efficiently find the important explanatory factors in a grouped manner. Different from traditional batch-mode group lasso algorithms, which suffer from the inefficiency and poor scalability, our proposed algorithm performs in an online mode and scales well: at each iteration one can update the weight vector according t...

The reproduction of a sound field measured using a microphone array is an active topic of research. To this end, loudspeaker and microphone arrays are used. Classical methods rely on spatial transforms (such as spherical Fourier transform for Ambisonics) or pressure matching using least-mean-square formulation. For both methods, all the reproduction sources (i.e. loudspeakers) will typically be...

The Lasso achieves variance reduction and variable selection by solving an 1-regularized least squares problem. Huang (2003) claims that ‘there always exists an interval of regularization parameter values such that the corresponding mean squared prediction error for the Lasso estimator is smaller than for the ordinary least square estimator’. This result is correct. However, its proof in Huang ...

BACKGROUND Performance measurement is essential to the management of health care organizations to which efficiency is per se a vital indicator. Present study accordingly aims to measure the efficiency of hospitals employing two distinct methods. METHODS Data Envelopment Analysis and Pabon Lasso Model were jointly applied to calculate the efficiency of all general hospitals located in Iranian ...

Abstract: Ordering of regression or classification coefficients occurs in many real-world applications. Fused Lasso exploits this ordering by explicitly regularizing the differences between neighboring coefficients through an l1 norm regularizer. However, due to nonseparability and nonsmoothness of the regularization term, solving the fused Lasso problem is computationally demanding. Existing s...

The lasso-based L1-graph is used in many applications since it can effectively model a set of data points as a graph. The lasso is a popular regression approach and the L1-graph represents data points as nodes by using the regression result. More specifically, by solving the L1-optimization problem of the lasso, the sparse regression coefficients are used to obtain the weights of the edges in t...

Professors McShane and Wyner have written a thought-provoking paper that intends to challenge some of the conventional wisdom in the paleoclimate literature. Rather than commenting on the merits of the entire methodology we focus on one topic. Namely, we discuss theoretical and practical aspects of the use of the least absolute shrinkage and selection operator [Tibshirani (1996)], more popularl...

We consider the linear regression problem. We propose the S-Lasso procedure to estimate the unknown regression parameters. This estimator enjoys sparsity of the representation while taking into account correlation between successive covariates (or predictors). The study covers the case when p ≫ n, i.e. the number of covariates is much larger than the number of observations. In the theoretical p...

Both canonical and alternative splicing of RNAs are governed by intronic sequence elements and produce transient lariat structures fastened by branch points within introns. To map precisely the location of branch points on a genomic scale, we developed LaSSO (Lariat Sequence Site Origin), a data-driven algorithm which utilizes RNA-seq data. Using fission yeast cells lacking the debranching enzy...

Lasso proved to be an extremely successful technique for simultaneous estimation and variable selection. However lasso has two major drawbacks. First, it does not capture any grouping effect and secondly in some situations lasso solutions are inconsistent. To overcome inconsistency recently adaptive lasso was proposed where adaptive weights are used for penalizing different coefficients. Adapti...