Kernel-based tensor partial least squares for reconstruction of limb movements

Qibin Zhao, Guoxu Zhou, Tulay Adali, Liqing Zhang, Andrzej Cichocki

Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

11 Цитирования (Scopus)

Аннотация

We present a new supervised tensor regression method based on multi-way array decompositions and kernel machines. The main issue in the development of a kernel-based framework for tensorial data is that the kernel functions have to be defined on tensor-valued input, which here is defined based on multi-mode product kernels and probabilistic generative models. This strategy enables taking into account the underlying multilinear structure during the learning process. Based on the defined kernels for tensorial data, we develop a kernel-based tensor partial least squares approach for regression. The effectiveness of our method is demonstrated by a real-world application, i.e., the reconstruction of 3D movement trajectories from electrocorticography signals recorded from a monkey brain.

Язык оригиналаАнглийский
Название основной публикации2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Страницы3577-3581
Число страниц5
DOI
СостояниеОпубликовано - 18 окт. 2013
Опубликовано для внешнего пользованияДа
Событие2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Канада
Продолжительность: 26 мая 201331 мая 2013

Серия публикаций

НазваниеICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (печатное издание)1520-6149

Конференция

Конференция2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Страна/TерриторияКанада
ГородVancouver, BC
Период26/05/1331/05/13

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