双対平坦空間におけるLeast Angle Regressionと情報量規準

By 廣瀬善大, 駒木文保. 2009

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Citations in the corpus

Forward stability and model path selection (2021, positive): “We ﬁrst examine the diabetes dataset provided in Efron et al. (2004), which was originally provided to demonstrate least-angle regression (LARS) in a sparse linear regression regime but more recently has been used to demonstrate the behavior of CVC (Lei 2020).”
Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso (2017, neutral): “For example: • forward stepwise can be seen as a “locally optimal” version of best subset selection, updating the active set by one variable at each step, instead of re-optimizing over all possible subsets of a given size; in turn, the lasso can be seen as a more “democratic” version of forward step”
Joint covariate selection and joint subspace selection for multiple classification problems (2010, background): “There are problems—including 1-regularized least-squares regression and the 1- and 2-regularized support vector machines—for which this path is piecewise linear and for which it is possible to follow the path exactly (Efron et al. 2004; Rosset and Zhu 2007).”
Stability selection (2008, methodology): “Using these rescaled variables, the standard lasso is solved, using for example the algorithm LARS (Efron et al., 2004) or fast co-ordinatewise approaches (Meier et al.”
Low-N protein engineering with data-efficient deep learning (2020, methodology): “This is L1-penalized linear regression implemented using the Least Angle Regression algorithm 78 .”
Online Learning for Matrix Factorization and Sparse Coding (2009, positive): “However, having D T Λ D Λ invertible is a common assumption in linear regression and in methods such as the LARS algorithm aimed at solving Eq.”
Multi-Label Prediction via Compressed Sensing (2009, methodology): “We tested the greedy and iterative reconstruction algorithms described earlier (OMP, FoBa, and CoSaMP) as well as a path-following version of Lasso based on LARS [21].”
Consistency of the group Lasso and multiple kernel learning (2007, unknown): “They are based on the piecewise linearity of the set of solutions as a function of the regularization parameter l n (Efron et al., 2004).”
An extensive experimental survey of regression methods (2019, methodology): “method [16] which is less greedy than the typical forward selection 813 methods.”
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation (2019, methodology): “For example, in LASSO, one common approach is to use Least Angle Regression (Efron et al., 2004).”
Online dictionary learning for sparse coding (2009, methodology): “However, the columns of learned dictionaries are in general highly correlated, and we have empirically observed that a Cholesky-based implementation of the LARS-Lasso algorithm, an homotopy method (Osborne et al., 2000; Efron et al., 2004) that provides the whole regularization path—that is, the sol”
On Similarity Preserving Feature Selection (2013, methodology): “Algorithms of the embedded model, e.g., C4.5 [29] and LARS [12], incorporate feature selection as a part of the learning process, and use the objective function of the learning model to guide searching for relevant features.”
Group SCAD regression analysis for microarray time course gene expression data (2007, unknown): “[7] Efron B, Hastie T, Johnstone I and Tibshirani R (2004): Least angle regression Annals of Statistics, 32, 407499.”
Discriminative coupled dictionary hashing for fast cross-media retrieval (2014, methodology): “(12). which can be solved in O ( =1 NKp m ) time using the LARS algorithm [4].”
Feature selection in machine learning: A new perspective (2018, methodology): “Lasso-based feature selection methods have well stability, including Lasso for regression model [57], Lars [55], Adaptive-lasso [58], elastic net [59], and so on.”
L1‐regularization path algorithm for generalized linear models (2007, methodology): “Efron et al. (2004) suggested an efficient algorithm to determine the exact piecewise linear coefficient paths for the lasso; see Osborne et al.”