From Lasso regression to Feature vector machine

By Fan Li, Yiming Yang, Eric P. Xing. 2005

108 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Qiang Yang
2. Yu Zhang
3. Mingsheng Long
4. Ying Wei
5. Eric P. Xing
6. Emilio Carrizosa
7. Dacheng Tao
8. Yoshua Bengio

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).”
• High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso (2012, methodology): “Note, since FVM uses d × d dimensional Hessian matrix D , it is especially useful when the number of training samples n is much bigger than that of features d .”
• An Equivalence between the Lasso and Support Vector Machines (2013, methodology): “In a different line of research, (Li et al., 2005) have studied the relation of a dual variant of the Lasso to the primal of the so called potential SVM originally proposed by (Hochreiter & Obermayer, 2004), which is not a classifier but a specialized method of feature selection.”
• Sparse multitask regression for identifying common mechanism of response to therapeutic targets (2010, methodology): “The L1-regularization typically leads to sparse learning models, and has been independently discovered in several research areas such as regression shrinkage and variable selection [29], basis pursuit [7], compressive sensing [6], and feature vector machine [19].”
• Deep Neural Networks for High Dimension, Low Sample Size Data (2017, methodology): “Feature Vector Machine (FVM) [Li et al., 2005] nonlinearly transforms each feature and label using the kernel function and it learns a sparse model using the new features and labels to achieve nonlinear feature selection.”