Active Metric Learning for Supervised Classification

03/28/2018
by   Krishnan Kumaran, et al.
2

Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extensively studied in the literature. Additionally, we generalize and improve upon leading methods by removing reliance on pre-designated "target neighbors," "triplets," and "similarity pairs." Another salient feature of our method is its ability to enable active learning by recommending precise regions to sample after an optimal metric is computed to improve classification performance. This targeted acquisition can significantly reduce computational burden by ensuring training data completeness, representativeness, and economy. We demonstrate classification and computational performance of the algorithms through several simple and intuitive examples, followed by results on real image and medical datasets.

READ FULL TEXT

page 6

page 7

page 8

page 10

page 12

research
02/09/2018

Learning Local Metrics and Influential Regions for Classification

The performance of distance-based classifiers heavily depends on the und...
research
04/15/2014

Sparse Compositional Metric Learning

We propose a new approach for metric learning by framing it as learning ...
research
05/20/2020

Batch Decorrelation for Active Metric Learning

We present an active learning strategy for training parametric models of...
research
06/02/2023

Mixed-type Distance Shrinkage and Selection for Clustering via Kernel Metric Learning

Distance-based clustering and classification are widely used in various ...
research
02/21/2017

Exemplar-Centered Supervised Shallow Parametric Data Embedding

Metric learning methods for dimensionality reduction in combination with...
research
07/31/2023

Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression

This paper examines various methods of computing uncertainty and diversi...
research
09/08/2020

Understanding and Exploiting Dependent Variables with Deep Metric Learning

Deep Metric Learning (DML) approaches learn to represent inputs to a low...

Please sign up or login with your details

Forgot password? Click here to reset