Sparse Learning
43 papers with code • 3 benchmarks • 3 datasets
Libraries
Use these libraries to find Sparse Learning models and implementationsMost implemented papers
SparseStep: Approximating the Counting Norm for Sparse Regularization
The SparseStep algorithm is presented for the estimation of a sparse parameter vector in the linear regression problem.
From safe screening rules to working sets for faster Lasso-type solvers
For the Lasso estimator a WS is a set of features, while for a Group Lasso it refers to a set of groups.
Learning task structure via sparsity grouped multitask learning
We further demonstrate that our proposed method recovers groups and the sparsity patterns in the task parameters accurately by extensive experiments.
Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low
We conduct an extensive theoretical analysis of the predictive properties of the proposed approach and provide justification for its superior predictive performance relative to best subset selection when the noise-level is high.
Sparse learning of stochastic dynamic equations
With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets.
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
In spite of the usefulness of $L_0$-based estimators and generic MIO solvers, there is a steep computational price to pay when compared to popular sparse learning algorithms (e. g., based on $L_1$ regularization).
Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking
To address this problem, this paper presents a novel approach to suppress background effects for RGB-T tracking.
Optimal approximation for unconstrained non-submodular minimization
Submodular function minimization is well studied, and existing algorithms solve it exactly or up to arbitrary accuracy.
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection.
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost
Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance.