Can I read Unsupervised Similarity Learning through Rank Correlation and kNN Sets on EtoBox?

Unsupervised Similarity Learning through Rank Correlation and kNN Sets by Lucas Pascotti Valem; Carlos Renan De Oliveira; Daniel Carlos Guimarães Pedronette; Jurandy Almeida is a Computer Science article available to read on EtoBox.

What is Unsupervised Similarity Learning through Rank Correlation and kNN Sets about?

The increasing amount of multimedia data collections available today evinces the pressing need for methods capable of indexing and retrieving this content. Despite the continuous advances in multimedia features and representation models, to establish an effective measure for comparing different multimedia objects still remains a challenging task. While supervised and semi-supervised techniques made relevant advances on similarity learning tasks, scenarios where labeled data are non-existent require different strategies. In such situations, unsupervised learning has been established as a promising solution, capable of considering the contextual information and the dataset structure for computing new similarity/dissimilarity measures. This article extends a recent unsupervised learning algorithm that uses an iterative re-ranking strategy to take advantage of different __k__ -Nearest Neighbors (kNN) sets and rank correlation measures. Two novel approaches are proposed for computing the kNN sets and their corresponding top- __k__ lists. The proposed approaches were validated in conjunction with various rank correlation measures, yielding superior effectiveness results in comparison wit

Who reads Unsupervised Similarity Learning through Rank Correlation and kNN Sets?

Author

Lucas Pascotti Valem; Carlos Renan De Oliveira; Daniel Carlos Guimarães Pedronette; Jurandy Almeida

Publisher

ACM

Published

2018

Language

EN

Field

Computer Science (Physical Sciences)