Fast sparse regression and classification

By Jerome H. Friedman. 2012

288 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Statistics and Probability

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Citations in the corpus

Best Subset Selection via a Modern Optimization Lens (2015, background): “…used to “bridge” the gap between the convex 1 penalty and the combinatorial 0 penalty [Candès, Wakin and Boyd (2008), Fan and Li (2001), Frank and Friedman (1993), Friedman (2008), Mazumder, Friedman and Hastie (2011), Zhang (2010a, 2010b), Zhang and Huang (2008), Zou (2006), Zou and Li (2008)].”
Spectral Regularization Algorithms for Learning Large Incomplete Matrices (2010, methodology): “We discuss simulations and experimental studies in Section 7 and application to the Netflix data in Section 8.”
Regularization Paths for Generalized Linear Models via Coordinate Descent (2010, methodology): “Methods using non-concave penalties, such as SCAD (Fan and Li 2005) and Friedman’s generalized elastic net (Friedman 2008), enforce more severe variable selection than the lasso.”
Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions (2012, background): “…[19], lasso penalized generalized linear models [12], the Dantzig selector [20], a slightly modified version of the lasso, generalized elastic net [21]; smoothly clipped absolute deviation procedures that reduce bias and yield continuous solutions (SCAD) [14], reproducing kernel Hilbert spaces…”
Estimation of oblique structure via penalized likelihood factor analysis (2013, unknown): “In regression analysis, a number of researchers have proposed fast algorithms to obtain the entire solutions (e.g., Least angle regression, Efron et al. 2004; Coordinate descent algorithm, Friedman et al. 2007; Generalized path seeking, Friedman 2012).”
Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption (2017, methodology): “By imposing sparse constraints on local classiﬁer updates ( e.g. , [24, 56, 65]), the DPHE exploits the sparsity to encrypt high-dimensional classiﬁers efﬁciently.”
Retail forecasting: Research and practice (2019, methodology): “Penalized likelihood methods have been successfully developed over the last decades to cope with high dimensionality (Friedman, 2012; Tibshirani, 2011). A number of recent studies on product demand forecasting are all based on this method. For example, Gür Ali (2013) proposed a "Driver Moderator" me”
Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm (2015, background): “…)2 Laplace [20] λ (1 nonconvex surrogates of L 0 -norm have been proposed, including L p -norm ( 0 p 1 ) [13], Smoothly Clipped Absolute Deviation (SCAD) [14], Logarithm [15], Minimax Concave Penalty (MCP) [16], Capped L 1 [17], Exponential-Type Penalty (ETP) [18], Geman [19] and Laplace [20].”
Generalized Nonconvex Nonsmooth Low-Rank Minimization (2014, methodology): “(c) Logarithm Penalty [12] 0 2 4 6 0 0.”
SparseNet: Coordinate Descent With Nonconvex Penalties (2011, background): “…members (a) The iy penalty given by y) = X\t\Y for y e [0,1], also referred to as the bridge or power family (Frank and Friedman 1993; Friedman 2008). compute the exact solution path for Q(/}) as a (b) The log-penalty is a generalization of the elastic net fam ily (Friedman 2008)…”