Modeling Dependencies in Multiple Parallel Data Streams with Hyperdimensional Computing

This work presents an approach for modeling statistical dependencies in multivariate discrete sequences by using hyperdimensional random vectors. The system takes any number of parallel sequences as inputs and learns to predict the future states of these streams using the mutual dependencies between...

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Bibliographic Details
Published in:IEEE signal processing letters 2014-07, Vol.21 (7), p.899-903
Main Authors: Rasanen, Okko, Kakouros, Sofoklis
Format: Article
Language:English
Subjects:
Activity recognition
Context
Data models
Data transmission
Encoding
hyperdimensional computing
machine learning
Mathematical analysis
Mathematical models
multimodal processing
Recognition
Sensor systems
Signal processing
Streams
Tasks
Temperature sensors
Vectors
Vectors (mathematics)
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Summary:This work presents an approach for modeling statistical dependencies in multivariate discrete sequences by using hyperdimensional random vectors. The system takes any number of parallel sequences as inputs and learns to predict the future states of these streams using the mutual dependencies between the inputs. Performance of the system is tested in an activity recognition task with data from multiple worn sensors. The results show that the approach outperforms the existing baseline results in the task and demonstrate that the system is capable to account for the varying reliability of different input streams.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2014.2320573