Large-scale Nonlinear Variable Selection via Kernel Random Features

Gregorova, Magda; Ramapuram, Jason; Kalousis, Alexandros; Marchand-Maillet, Stéphane

doi:10.48550/arxiv.1804.07169

Cited by 1 publication

(5 citation statements)

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“…We then apply STG and evaluate the classification accuracy and the number of selected features. We use the architecture [200,50,10] with tanh activations. The experiment was repeated 10 times, the extracted features and accuracies were consistent over 20 trials.…”

Section: Sparse Handwritten Digits Classificationmentioning

confidence: 99%

“…(b) The comparison of accuracy and sparsity level performance for λ in the range of [10 −3 , 10 −2 ] between using our proposed method (STG) and its variant using the Hard-Concrete (HC) distribution. [50]. Following the same format as [50], the following functions are used to generate synthetic data: (SE1: 100/5) y = sin (x 1 + x 3 ) 2 sin (x 7 x 8 x 9 ) + N (0, 0.1).…”

Section: Regression Using Synthetic and Real Datasetsmentioning

confidence: 99%

“…[50]. Following the same format as [50], the following functions are used to generate synthetic data: (SE1: 100/5) y = sin (x 1 + x 3 ) 2 sin (x 7 x 8 x 9 ) + N (0, 0.1). (SE2: 18/5) y = log(( 15 s=11 x s ) 2 ) + N (0, 0.1).…”

Section: Regression Using Synthetic and Real Datasetsmentioning

confidence: 99%

“…For each dataset, we generate 30 different replications and randomly split the data into train, validation, and test set. For data preparation, we use the code publicly made available by [50]. The root mean squared error on the test set averaged over 30 random replicated datasets are reported in Table 1.…”

Section: Regression Using Synthetic and Real Datasetsmentioning

confidence: 99%

“…N is 1000 for SE1,2,3 and 6000 for RCP. The values for LASSO and SRFF are borrowed from [50]. namely the Naive and regulatory T-cells.…”

Section: Purified Populations Of Peripheral Blood Monocytes (Pbmcs)mentioning

confidence: 99%

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Feature Selection using Stochastic Gates

Yamada,

Lindenbaum,

Negahban

et al. 2018

Preprint

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Feature selection problems have been extensively studied for linear estimation, for instance, Lasso, but less emphasis has been placed on feature selection for non-linear functions. In this study, we propose a method for feature selection in high-dimensional non-linear function estimation problems. The new procedure is based on minimizing the 0 norm of the vector of indicator variables that represent if a feature is selected or not. Our approach relies on the continuous relaxation of Bernoulli distributions, which allows our model to learn the parameters of the approximate Bernoulli distributions via gradient descent. This general framework simultaneously minimizes a loss function while selecting relevant features. Furthermore, we provide an informationtheoretic justification of incorporating Bernoulli distribution into our approach and demonstrate the potential of the approach on synthetic and real-life applications.

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Section: Sparse Handwritten Digits Classificationmentioning

confidence: 99%

Section: Regression Using Synthetic and Real Datasetsmentioning

confidence: 99%

Section: Regression Using Synthetic and Real Datasetsmentioning

confidence: 99%

Section: Regression Using Synthetic and Real Datasetsmentioning

confidence: 99%

“…N is 1000 for SE1,2,3 and 6000 for RCP. The values for LASSO and SRFF are borrowed from [50]. namely the Naive and regulatory T-cells.…”

Section: Purified Populations Of Peripheral Blood Monocytes (Pbmcs)mentioning

confidence: 99%

See 3 more Smart Citations

Feature Selection using Stochastic Gates

Yamada,

Lindenbaum,

Negahban

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

Large-scale Nonlinear Variable Selection via Kernel Random Features

Cited by 1 publication

References 0 publications

Feature Selection using Stochastic Gates

Feature Selection using Stochastic Gates

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