Active Nearest-neighbor Learning In Metric Spaces

Abstract

We propose a pool-based non-parametric active learning algorithm for general metric spaces, called MArgin Regularized Metric Active Nearest Neighbor (MARMANN), which outputs a nearest-neighbor classifier. We give prediction error guarantees that depend on the noisy-margin properties of the input sample, and are competitive with those obtained by previously proposed passive learners. We prove that the label complexity of MARMANN is significantly lower than that of any passive learner with similar error guarantees. MARMANN is based on a generalized sample compression scheme, and a new label-efficient active model-selection procedure.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keykontorovich2016active

Related papers

• Robust Mahalanobis Metric Learning Via Geometric Approximation Algorithms (2019)0.00
• Local Distance Metric Learning For Nearest Neighbor Algorithm (2018)0.00
• Adaptive Neighborhood Metric Learning (2022)10.07
• Feasibility Based Large Margin Nearest Neighbor Metric Learning (2016)0.00
• Interpretable Locally Adaptive Nearest Neighbors (2020)3.58
• Adaptive Nearest Neighbor: A General Framework For Distance Metric Learning (2019)0.00
• Active Metric Learning And Classification Using Similarity Queries (2022)0.00
• Learning Nearest Neighbor Graphs From Noisy Distance Samples (2019)0.00