Timezone: »
Talk
Manifold Learning
Lawrence Saul
How can we detect low dimensional structure in high dimensional data? Sam and I worked feverishly on this problem for a number of years. We were particularly interested in analyzing high dimensional data that lies on or near a low dimensional manifold. I will describe the algorithm, locally linear embedding (LLE), that we developed for this problem. I will conclude by relating LLE to more recent work in manifold learning and sketching some future directions for research.
Author Information
Lawrence Saul (UC San Diego)
More from the Same Authors
-
2012 Poster: Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning »
Matthew F Der · Lawrence Saul -
2011 Poster: Maximum Covariance Unfolding : Manifold Learning for Bimodal Data »
Vijay Mahadevan · Chi Wah Wong · Jose Costa Pereira · Tom Liu · Nuno Vasconcelos · Lawrence Saul -
2010 Poster: Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development »
Diane Hu · Laurens van der Maaten · Youngmin Cho · Lawrence Saul · Sorin Lerner -
2009 Poster: Kernel Methods for Deep Learning »
Youngmin Cho · Lawrence Saul -
2006 Poster: Large Margin Gaussian Mixture Models for Automatic Speech Recognition »
Fei Sha · Lawrence Saul -
2006 Talk: Large Margin Gaussian Mixture Models for Automatic Speech Recognition »
Fei Sha · Lawrence Saul -
2006 Poster: Graph Regularization for Maximum Variance Unfolding with an Application to Sensor Localization »
Kilian Q Weinberger · Fei Sha · Qihui Zhu · Lawrence Saul
