The generalized LASSO

By Volker Roth. 2004

343 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition

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Citations in the corpus

Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, methodology): “To address this issue, models have been proposed aimed at increasing the sparsity of the classi er, as is done with the Lasso, see Li et al. (2006) and Roth (2004).”
From Lasso regression to Feature vector machine (2005, methodology): “To address this limitation, [Roth, 2004] proposed “generalized Lasso regressions” (GLR) by introducing kernels.”
Sparse canonical correlation analysis (2009, methodology): “4.2. Furthermore, turning the constraint � Ke �= 1i nto� e �= 1 links with similar procedures in supervised classification (e.g., Roth 2004 ).”
Label-Noise Robust Logistic Regression and Its Applications (2012, methodology): “Akin to generalised Lasso [24], we will employ L1-regularisation terms on each component of w.”
Gradient boosted feature selection (2014, background): “H.3 [Information Storage and Retrieval]: Miscellaneous; I.5.2 [Pattern Recognition]: Design Methodology—Feature evaluation and selection”
Stopping stepwise: Why stepwise and similar selection methods are bad, and what you should use (2007, methodology): “However, it has been applied with a number of other types of model, including Cox proportional hazard models Tibshirani (1997), binomial logistic Roth (2005), Shevade & Keerthi (2003) and multinomial B. Krishnapuram & Hartemink (2005) models, and others.”
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso (2012, methodology): “To handle non-linearity, the instance-wise non-linear Lasso was introduced (Roth, 2004), where the original instance x is transformed by a non-linear function ψ ( ) : R d → R d ′ .”
Hyperspectral image classification via kernel sparse representation (2011, methodology): “Although the proposed approach has a similar formulation as previous kernel regression approaches with a sparse prior such as kernel matching pursuit [33], kernel basis pursuit [34], and generalized LASSO [38], the underlying ideas are quite different.”
An Equivalence between the Lasso and Support Vector Machines (2013, methodology): “Traditional kernel regression techniques (Smola & Sch¨olkopf, 2004; Saun-ders et al., 1998; Roth, 2004) try to learn a real-valued function f from the space R d of the datapoints, such that the resulting real value for each datapoint approximates some observed value.”
Graph Regularized Sparse Coding for Image Representation (2011, background): “Several approaches have been proposed to solve the problem of this form [43]–[47].”
Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning (2008, methodology): “kernel: regular Hilbertian regularization (L2), same gree dy approach as earlier (greedy), regularization by thel(1)-norm directly on the vector α, a strategy which is sometimes used in the context of sparse kernel learning [14] but does not use the Hilbertian s ructure of the kernel (lassoα), multi”
A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification (2010, background): “Roth (2004) studied general loss functions including logistic loss.”
Least angle and ℓ1 penalized regression: A review (2008, methodology): “(15) Several authors discuss extensions of LARS to these models: generalized linear models (Lokhorst, 1999; Roth, 2004; Madigan and Ridgeway, 2004; Rosset, 2005; Park and Hastie, 2007; Keerthi and Shevade, 2007) and Cox regression (Tibshirani, 1997; Gui and Li, 2005; Park and Hastie, 2007).”
Partially supervised feature selection with regularized linear models (2009, methodology): “We also plan to study to which extent partial supervision could be applied to other regularized models, such as the generalized LASSO (Roth, 2004) or Huberized SVMs (Wang et al., 2007).”
Efficient L1 Regularized Logistic Regression (2006, methodology): “Roth (2004) proposed an algorithm called generalized LASSO that extends a LASSO algorithm proposed by Osborne et al. (2000).”
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression (2007, methodology): “, 2006; Lokhorst, 1999), a generalized LASSO method (Roth, 2004) that extends the LASSO method proposed in Osborne et al.”