Graph-Sparse Logistic Regression

12/15/2017
by   Alexander LeNail, et al.
0

We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We val- idate this algorithm against synthetic data and benchmark it against L1-regularized Logistic Regression. We then explore our technique in the bioinformatics context of proteomics data on the interactome graph. We make all our experimental code public and provide GSLR as an open source package.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/24/2014

Distributed Coordinate Descent for L1-regularized Logistic Regression

Solving logistic regression with L1-regularization in distributed settin...
research
02/21/2020

PIANO: A Fast Parallel Iterative Algorithm for Multinomial and Sparse Multinomial Logistic Regression

Multinomial Logistic Regression is a well-studied tool for classificatio...
research
10/25/2014

An Aggregation Method for Sparse Logistic Regression

L_1 regularized logistic regression has now become a workhorse of data m...
research
12/03/2019

A Hidden Variables Approach to Multilabel Logistic Regression

Multilabel classification is an important problem in a wide range of dom...
research
05/22/2018

On Coresets for Logistic Regression

Coresets are one of the central methods to facilitate the analysis of la...
research
03/08/2017

Sparse Quadratic Logistic Regression in Sub-quadratic Time

We consider support recovery in the quadratic logistic regression settin...
research
09/24/2019

Churn Prediction with Sequential Data and Deep Neural Networks. A Comparative Analysis

Off-the-shelf machine learning algorithms for prediction such as regular...

Please sign up or login with your details

Forgot password? Click here to reset