METHOD AND SYSTEM FOR MULTI-SENSOR FUSION IN THE PRESENCE OF MISSING AND NOISY LABELS

This disclosure relates to a method and system for multi-sensor fusion in the presence of missing and noisy labels. Prior methods for multi-sensor fusion do not estimate and correct labels for learning effective models in semi-supervised learning methods. Embodiments of the present disclosure provid...

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Bibliographische Detailangaben
Hauptverfasser:	SAHU, Saurabh, CHANDRA, Mariswamy Girish, KUMAR, Kriti, KUMAR, Achanna Anil, MAJUMDAR, Angshul
Format:	Patent
Sprache:	eng ; fre ; ger
Schlagworte:	BLASTING CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ENGINEERING ELEMENTS AND UNITS GEARING GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVEFUNCTIONING OF MACHINES OR INSTALLATIONS HEATING LIGHTING MECHANICAL ENGINEERING PHYSICS THERMAL INSULATION IN GENERAL WEAPONS
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Beschreibung
Zusammenfassung:	This disclosure relates to a method and system for multi-sensor fusion in the presence of missing and noisy labels. Prior methods for multi-sensor fusion do not estimate and correct labels for learning effective models in semi-supervised learning methods. Embodiments of the present disclosure provides a method for learning robust sensor-specific autoencoder based fusion model by utilizing a graph structure to perform label propagation and correction. In the disclosed Graph regularized AutoFuse (GAF) method latent representation for each sensor is learnt using the sensor-specific autoencoders. Further these latent representations are combined and fed to a classifier for multi-class classification. The disclosure presents a joint optimization formulation for multi-sensor fusion where label propagation and correction, sensor-specific learning and classification are executed together.