Visualizing Folksonomies using Machine Learning Algorithms

This paper, written for my Adv. Machine Learning class, investigates using Semidefinite Embedding (SDE) to visualize data collected from a folksonomy. The del.icio.us social bookmarking service is a perfect example of a folksonomy; a community of users label websites with descriptive tags. Each tag exists in a high-dimensional space corresponding to the frequency of use of that tag among all the users of the system. We are motivated by the following question: can we find a simple low-dimensional structure for these tags that captures the significant relationships inherent in the data? In this paper we explore Semidefinite Embedding, an algorithm for non-linear dimensionality reduction, and its application to visualizing folksonomic systems, focusing on the effects of specifying different levels of connectivity for the data and the heuristics which can be used to find the best parameters for the algorithm. (Full paper found here)