Cognitively Motivated Novelty Detection in Video Data Streams

2007 ◽  
pp. 209-233
Author(s):  
James M. Kang ◽  
Muhammad Aurangzeb Ahmad ◽  
Ankur Teredesai ◽  
Roger Gaborski
Keyword(s):  
Data Streams ◽  
Novelty Detection ◽  
Video Data

Online Clustering for Novelty Detection and Concept Drift in Data Streams

2019 ◽  
pp. 448-459
Author(s):  
Kemilly Dearo Garcia ◽  
Mannes Poel ◽  
Joost N. Kok ◽  
André C. P. L. F. de Carvalho
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
Novelty Detection ◽  
Online Clustering

Novelty Detection and Online Learning for Chunk Data Streams

2020 ◽  
pp. 1-1
Author(s):  
Yi Wang ◽  
Yi Ding ◽  
Xiangjian He ◽  
Xin Fan ◽  
Chi Lin ◽  
...  
Keyword(s):  
Online Learning ◽  
Data Streams ◽  
Novelty Detection

scholarly journals Evaluation of Multiclass Novelty Detection Algorithms for Data Streams

2015 ◽  
Vol 27 (11) ◽  
pp. 2961-2973 ◽  
Author(s):  
Elaine Ribeiro de Faria ◽  
Isabel Ribeiro Goncalves ◽  
Jo ao Gama ◽  
Andre Carlos Ponce de Leon Ferreira Carvalho
Keyword(s):  
Data Streams ◽  
Novelty Detection ◽  
Detection Algorithms

scholarly journals An adaptive algorithm for anomaly and novelty detection in evolving data streams

2018 ◽  
Vol 32 (6) ◽  
pp. 1597-1633 ◽  
Author(s):  
Mohamed-Rafik Bouguelia ◽  
Slawomir Nowaczyk ◽  
Amir H. Payberah
Keyword(s):  
Data Streams ◽  
Adaptive Algorithm ◽  
Novelty Detection ◽  
Evolving Data

scholarly journals MINAS: multiclass learning algorithm for novelty detection in data streams

2015 ◽  
Vol 30 (3) ◽  
pp. 640-680 ◽  
Author(s):  
Elaine Ribeiro de Faria ◽  
André Carlos Ponce de Leon Ferreira Carvalho ◽  
João Gama
Keyword(s):  
Data Streams ◽  
Learning Algorithm ◽  
Novelty Detection

Novelty detection with application to data streams

2009 ◽  
Vol 13 (3) ◽  
pp. 405-422 ◽  
Author(s):  
Eduardo J. Spinosa ◽  
André Ponce de Leon F. de Carvalho ◽  
João Gama
Keyword(s):  
Data Streams ◽  
Novelty Detection

scholarly journals Learning neural decoders without labels using multiple data streams

2021 ◽  
Author(s):  
Steven M. Peterson ◽  
Rajesh P. N. Rao ◽  
Bingni W. Brunton
Keyword(s):  
Real World ◽  
Data Streams ◽  
Video Data ◽  
Neural Decoding ◽  
Computer Interfaces ◽  
Multiple Data ◽  
Real World Applications ◽  
Recorded Data ◽  
Robust Adaptive ◽  
Decoding Accuracy

AbstractRecent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. One intriguing alternative uses self-supervised models that share self-generated pseudo-labels between two data streams; such models have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding. Here, we learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to decode movements from brain recordings; these decoders are compared to supervised and unimodal, self-supervised models. We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we develop decoders trained on three modalities that match or slightly exceed the performance of supervised models, achieving state-of-the-art neural decoding accuracy. Cross-modal decoding is a flexible, promising approach for robust, adaptive neural decoding in real-world applications without any labels.

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