Efficient Data Analytics on Augmented Similarity Triplets

TL;DR

This paper introduces triplets augmentation, a scalable method for enhancing triplet-based data analysis by inferring implicit information, leading to improved performance in kernel-based and kernel-free analytics, especially on large and noisy datasets.

Contribution

It proposes a novel triplets augmentation technique and algorithms for data analysis that operate directly on triplets, avoiding kernel evaluations and improving scalability and robustness.

Findings

Outperforms existing techniques in accuracy and efficiency.

Effective on large-scale and noisy datasets.

Enhances kernel-based and kernel-free data analytics.

Abstract

Data analysis require a pairwise proximity measure over objects. Recent work has extended this to situations where the distance information between objects is given as comparison results of distances between three objects (triplets). Humans find the comparison tasks much easier than the exact distance computation and such data can be easily obtained in big quantity via crowd-sourcing. In this work, we propose triplets augmentation, an efficient method to extend the triplets data by inferring the hidden implicit information form the existing data. Triplets augmentation improves the quality of kernel-based and kernel-free data analytics. We also propose a novel set of algorithms for common data analysis tasks based on triplets. These methods work directly with triplets and avoid kernel evaluations, thus are scalable to big data. We demonstrate that our methods outperform the current best-known techniques and are robust to noisy data.