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Scalable and Privacy-Preserving Federated Principal Component Analysis

Publications associées (36)

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Transforming the feature vectors to improve HMM based cursive word recognition systems

Samy Bengio, Alessandro Vinciarelli

Although many Offline Cursive Word Recognition systems are based on HMMs, no attention was ever paid, to our knowledge, to the fact that the feature vectors are typically not in the most suitable form for modeling. They are most of the time correlated and ...

IDIAP2002

Offline Cursive Word Recognition using Continuous Density Hidden Markov Models trained with PCA or ICA Features

Samy Bengio, Alessandro Vinciarelli

This work presents an Offline Cursive Word Recognition System dealing with single writer samples. The system is a continuous density hiddden Markov model trained using either the raw data, or data transformed using Principal Component Analysis or Independe ...

IDIAP2001

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000

Mixture Models for Unsupervised and Supervised Learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

IDIAP2000

Mixture Models for Unsupervised and Supervised Learning

École Polytechnique Fédérale de Lausanne, Computer Science Department2000

Fourier dimensionality reduction for fast radio transients

Jean-Philippe Thiran, Yves Wiaux, Vijay Kartik, Ming Jiang

In the context of next-generation radio interferometers, we are facing a big challenge of how to economically process data. The classical dimensionality reduction technique, averaging visibilities on time, may dilute fast radio transients (FRT). We propose ...