# Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking

## TL;DR

This paper introduces a method for learning low-dimensional continuous criteria in large databases through interactive ranking, enabling users to efficiently organize thousands of data points with minimal labeling effort.

## Contribution

It presents a novel semi-supervised, active learning approach for interactive ranking that simplifies organizing large datasets along continuous criteria.

## Key findings

- Effective organization of thousands of data points using 1D and 2D sliders.
- Active data point suggestion improves ranking efficiency.
- Applicable to image and geometry datasets.

## Abstract

Large databases are often organized by hand-labeled metadata, or criteria, which are expensive to collect. We can use unsupervised learning to model database variation, but these models are often high dimensional, complex to parameterize, or require expert knowledge. We learn low-dimensional continuous criteria via interactive ranking, so that the novice user need only describe the relative ordering of examples. This is formed as semi-supervised label propagation in which we maximize the information gained from a limited number of examples. Further, we actively suggest data points to the user to rank in a more informative way than existing work. Our efficient approach allows users to interactively organize thousands of data points along 1D and 2D continuous sliders. We experiment with datasets of imagery and geometry to demonstrate that our tool is useful for quickly assessing and organizing the content of large databases.