Timezone: »
Spotlight
An algorithm is presented for online learning of rotations. The proposed algorithm involves matrix exponentiated gradient updates and is motivated by the Von Neumann divergence. The additive updates are skew-symmetric matrices with trace zero which comprise the Lie algebra of the rotation group. The orthogonality and unit determinant of the matrix parameter are preserved using matrix logarithms and exponentials and the algorithm lends itself to interesting interpretations in terms of the computational topology of the compact Lie groups. The stability and the computational complexity of the algorithm are discussed.
Author Information
Raman Arora (Johns Hopkins University)
Related Events (a corresponding poster, oral, or spotlight)
-
2009 Poster: On Learning Rotations »
Tue. Dec 8th 03:00 -- 07:59 AM Room
More from the Same Authors
-
2017 Poster: Stochastic Approximation for Canonical Correlation Analysis »
Raman Arora · Teodor Vanislavov Marinov · Poorya Mianjy · Nati Srebro -
2013 Poster: Stochastic Optimization of PCA with Capped MSG »
Raman Arora · Andrew Cotter · Nati Srebro