Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Towards google challenge: combining contextual and social information for web video categorization
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Image classification using the web graph
Proceedings of the international conference on Multimedia
Improving video classification via youtube video co-watch data
SBNMA '11 Proceedings of the 2011 ACM workshop on Social and behavioural networked media access
Exploiting social relations for sentiment analysis in microblogging
Proceedings of the sixth ACM international conference on Web search and data mining
Image context discovery from socially curated contents
Proceedings of the 21st ACM international conference on Multimedia
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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.