Learning Similarities and Dimensionality Reduction

Speaker: Dr. Kilian Weinberger, Yahoo! Research
Host: Prof. Fei ShaAbstract:
One of the most fundamental challenges of machine learning and artificial intelligence is the learning of suitable representations of data. Many machine learning algorithms assume that the data is presented in low dimensional vectorial form, where Euclidean distances reflect dissimilarities. Often this raw data format is far from optimal. Ideally one should be able to learn a "hand-tailored" representation of each particular data set for any given task. In this talk, I present three algorithms for learning compact representations that give rise to semantically meaningful similarity metrics. Each of the algorithms involves, at its core, a convex optimization problem that learns the new representation under meaningful constraints. This framework provides perfect reproducibility and theoretical guarantees. The three methods are most suitable for different data settings: Maximum Variance Unfolding reduces the dimensionality of data sets with underlying manifold structure. Taxonomy Embedding is a powerful tool for hierarchical document categorization. Large Margin Nearest Neighbor learns a robust metric for k-nearest neighbor classification. I present state-of-the-art classification results on several real world applications, including handwritten digit recognition on the MNIST corpus and document categorization on the OHSUMED medical journal data base. Biography:
Kilian Weinberger is a Research Scientist at Yahoo Research in Santa Clara, California. He works on next-generation spam filtering algorithms, multimedia search and machine learning with convex optimization. In 2007 he received a Ph.D. in Computer Science at the University of Pennsylvania under the supervision of Prof. Lawrence Saul. His work on supervised and unsupervised metric learning won several outstanding paper awards at CVPR, AISTATS and ICML. Prior to his doctoral studies he earned a first class honor BA in Mathematics and Computer Science from the University of Oxford.

Location: Seaver Science Library (SSL) - 150

Audiences: Everyone Is Invited

Contact: CS Colloquia