Modeling Dependencies in Multiple Parallel Data Streams with Hyperdimensional Computing

Research output: Contribution to journalArticleScientificpeer-review

26 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)899-903
JournalIEEE Signal Processing Letters
Issue number7
Publication statusPublished - 2014
MoE publication typeA1 Journal article-refereed


  • Activity recognition
  • hyperdimensional computing
  • machine learning
  • multimodal processing


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