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A Probabilistic Approach to Handle Missing Data for Multi-Sensory Activity Recognition

Related publications (42)

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Decision-based methods for early phase sustainable product design

Paul Xirouchakis, Razvan Alexandru Gheorghe

This paper presents a new method for evaluating product designs at their early stage of development according to different criteria and a case-study-based project. The goal is to select among imprecisely defined design alternatives in a multicriteria conte ...

2007

Towards Explaining the Success (Or Failure) of Fusion in Biometric Authentication

Samy Bengio, Norman Hoon Thian Poh

Biometric authentication is a process of verifying an identity claim using a person's behavioral and physiological characteristics. Due to vulnerability of the system to environmental noise and variation caused by the user, fusion of several biometric-enab ...

IDIAP2005

Image Replica Detection based on Binary Support Vector Classifier

Touradj Ebrahimi

In this paper, we present a system for image replica detection. More specifically, the technique is based on the extraction of 162 features corresponding to texture, color and gray-level characteristics. These features are then weighted and statistically n ...

2005

Error Handling in Multimodal Biometric Systems using Reliability Measures

In this paper, we present a framework for predicting and correcting classification decision errors based on modality reliability measures in a multimodal biometric system. In our experiments we use face and speech experts based on a recently proposed frame ...

IEEE2005

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...

2004

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

2004

Sequence Classification with Input-Output Hidden Markov Models

Samy Bengio, Silvia Chiappa

We present a training and testing method for Input-Output Hidden Markov Model that is particularly suited for classification of sequences in which class information accumulates over time. We discuss two such cases: the discrimination of mental tasks from s ...

IDIAP2004

Boosting HMMs with an application to speech recognition

Samy Bengio, Christos Dimitrakakis

IDIAP2003

Face Class Modeling in Eigenfaces Space

Jean-Philippe Thiran, Vlad Popovici

We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Another issue addressed is how to select the number of eigenfaces to achieve a good classification rate. As the experimental evidence show, genera ...

SPIE2003

Data utility modelling for mismatch reduction

In the "missing data" (MD) approach to noise robust automatic speech recognition (ASR), speech models are trained on clean data, and during recognition sections of spectral data dominated by noise are detected and treated as "missing". However, this all-or ...

2001