Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
The Journal of Machine Learning Research
Performance analysis of an adaptive algorithm for tracking dominantsubspaces
IEEE Transactions on Signal Processing
Relative loss bounds for single neurons
IEEE Transactions on Neural Networks
On-line estimation with the multivariate Gaussian distribution
COLT'07 Proceedings of the 20th annual conference on Learning theory
Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach
The Journal of Machine Learning Research
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We address the problem of online de-noising a stream of input points. We assume that the clean data is embedded in a linear subspace. We present two online algorithms for tracking subspaces and, as a consequence, de-noising. We also describe two regularization schemas which improve the resistance to noise. We analyze the algorithms in the loss bound model, and specify some of their properties. Preliminary simulations illustrate the usefulness of our algorithms.