188,106 research outputs found
DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data
With the rapid evolution of the Internet of Things, many real-world
applications utilize heterogeneously connected sensors to capture time-series
information. Edge-based machine learning (ML) methodologies are often employed
to analyze locally collected data. However, a fundamental issue across
data-driven ML approaches is distribution shift. It occurs when a model is
deployed on a data distribution different from what it was trained on, and can
substantially degrade model performance. Additionally, increasingly
sophisticated deep neural networks (DNNs) have been proposed to capture spatial
and temporal dependencies in multi-sensor time series data, requiring intensive
computational resources beyond the capacity of today's edge devices. While
brain-inspired hyperdimensional computing (HDC) has been introduced as a
lightweight solution for edge-based learning, existing HDCs are also vulnerable
to the distribution shift challenge. In this paper, we propose DOMINO, a novel
HDC learning framework addressing the distribution shift problem in noisy
multi-sensor time-series data. DOMINO leverages efficient and parallel matrix
operations on high-dimensional space to dynamically identify and filter out
domain-variant dimensions. Our evaluation on a wide range of multi-sensor time
series classification tasks shows that DOMINO achieves on average 2.04% higher
accuracy than state-of-the-art (SOTA) DNN-based domain generalization
techniques, and delivers 16.34x faster training and 2.89x faster inference.
More importantly, DOMINO performs notably better when learning from partially
labeled and highly imbalanced data, providing 10.93x higher robustness against
hardware noises than SOTA DNNs
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
One of the challenges in modeling cognitive events from electroencephalogram
(EEG) data is finding representations that are invariant to inter- and
intra-subject differences, as well as to inherent noise associated with such
data. Herein, we propose a novel approach for learning such representations
from multi-channel EEG time-series, and demonstrate its advantages in the
context of mental load classification task. First, we transform EEG activities
into a sequence of topology-preserving multi-spectral images, as opposed to
standard EEG analysis techniques that ignore such spatial information. Next, we
train a deep recurrent-convolutional network inspired by state-of-the-art video
classification to learn robust representations from the sequence of images. The
proposed approach is designed to preserve the spatial, spectral, and temporal
structure of EEG which leads to finding features that are less sensitive to
variations and distortions within each dimension. Empirical evaluation on the
cognitive load classification task demonstrated significant improvements in
classification accuracy over current state-of-the-art approaches in this field.Comment: To be published as a conference paper at ICLR 201
Making the Dynamic Time Warping Distance Warping-Invariant
The literature postulates that the dynamic time warping (dtw) distance can
cope with temporal variations but stores and processes time series in a form as
if the dtw-distance cannot cope with such variations. To address this
inconsistency, we first show that the dtw-distance is not warping-invariant.
The lack of warping-invariance contributes to the inconsistency mentioned above
and to a strange behavior. To eliminate these peculiarities, we convert the
dtw-distance to a warping-invariant semi-metric, called time-warp-invariant
(twi) distance. Empirical results suggest that the error rates of the twi and
dtw nearest-neighbor classifier are practically equivalent in a Bayesian sense.
However, the twi-distance requires less storage and computation time than the
dtw-distance for a broad range of problems. These results challenge the current
practice of applying the dtw-distance in nearest-neighbor classification and
suggest the proposed twi-distance as a more efficient and consistent option.Comment: arXiv admin note: substantial text overlap with arXiv:1808.0996
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