Exploiting socially-generated side information in dimensionality reduction

Alejandro Marcos Alvarez, Makoto Yamada, Akisato Kimura

Abstract: In this paper, we show how side information extracted from socially-curated data can be used within a dimensionality reduction method and to what extent this side information is beneficial to several tasks such as image classification, data visualization and image retrieval. The key idea is to incorporate side information of an image into a dimensionality reduction method. More specifically, we propose a dimensionality reduction method that can find an embedding transformation so that images with similar side information are close in the embedding space. We introduce three types of side information derived from user behavior. Through experiments on images from Pinterest, we show that incorporating socially-generated side information in a dimensionality reduction method benefits several image-related tasks such as image classification, data visualization and image retrieval.