Adaptive regression and model selection in data mining problems

By S. Bakin. 1999

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Citations in the corpus

Regularizing Text Categorization with Clusters of Words (2016, background): “Group lasso Bakin (1999) and later Yuan and Lin (2006) proposed an extension of L1 regularization to encourage groups of features to either go to zero (as a group) or not (as a group), introducing group sparsity in the model.”
Component selection and smoothing in multivariate nonparametric regression (2006, neutral): “Bakin (1999) contained some useful developments.”
Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors (2012, background): “Building oﬀ of earlier work by Bakin (1999), Yuan and Lin (2006) extended the ideas of penalized regression to the problem of grouped covariates.”
Let's Make Block Coordinate Descent Go Fast: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence (2017, methodology): “For example, consider a variant of problem h 1 where we use group L1-regularization [1], where B is a partition of the coordinates.”
Sparse Modeling for Image and Vision Processing (2014, background): “To the best of our knowledge, such a penalty appears in the early work of Grandvalet and Canu [1999] and Bakin [1999] for q = 2, and Turlach et al. [2005] for q =∞.”
Regularized Multivariate Regression for Identifying Master Predictors with Application to Integrative Genomics Study of Breast Cancer (2008, methodology): “In the case of multiple regression setting, a similar penalty corresponding to α = 2 is proposed by Bakin (1999) and by Yuan and Lin (2006) for the selection of grouped variables, which corresponds to the blockwise additive penalty in Antoniadis and Fan (2001) for wavelet shrinkage.”
Parsimonious additive models (2007, methodology): “(13) It has two unitary eigenvalues corresponding to the constant and linear functions (its projection part), and n − 2 nonnegative eigenvalues strictly smaller than 1 corresponding to higher-order functions (its shrinking part).”
Simultaneous Variable Selection (2005, methodology): “Bakin (1999) noted that the use of α = 2 can be interpreted as a hybrid between the LASSO (if p = m, i.e., each σl contains exactly one index) and ridge regression (if p = 1 and σl = σ for the entire set of indices).”
The group lasso for logistic regression (2008, methodology): “Keywords: Categorical data; Co-ordinate descent algorithm; DNA splice site; Group variable selection; High dimensional generalized linear model; Penalized likelihood”
Projected Newton-type methods in machine learning (2011, background): “The regularizer in (33) is referred to as a group regularizer (`1,2-regularizer), since it encourages sparsity in terms of groups of variables, and dates back to (Bakin, 1999).”
11 Projected Newton-type Methods in Machine Learning (2011, background): “(33) The regularizer in (33) is referred to as a group regularizer (`1,2-regularizer), since it encourages sparsity in terms of groups of variables, and dates back to (Bakin, 1999).”
Towards Integrative Machine Learning and Knowledge Extraction (2017, methodology): “For instance, the Census Bureau’s Person Identification Validation System is a production system that uses PRL [11,12] for matching person records arising from censuses records, survey datasets, administrative records, or commercial files.”
Structured Sparsity in Structured Prediction (2011, methodology): “We consider the following group-Lasso regularizers: These regularizers were ﬁrst proposed by Bakin (1999) and Yuan and Lin (2006) in the context of regression.”
A Selective Review of Group Selection in High-Dimensional Models. (2012, methodology): “Bakin (1999) proposed the group LASSO and a computational algorithm.”
Model selection and estimation in regression with grouped variables (2006, methodology): “Bakin (1999) proposed expression (2.1) as an extension of the lasso for selecting groups of variables and proposed a computational algorithm.”
Component selection and smoothing in smoothing spline analysis of variance models -- COSSO (2003, background): “Bakin (1999) contained some useful developments.”
Dimension reduction and coefficient estimation in multivariate linear regression (2007, background): “From equation (1), the jth response can be modelled by Yj =XBj +Ej, where Bj ∈Rp, j =1, . . . , q.”