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Takehisa YAIRI
Takehisa YAIRI
The University of Tokyo, Research Center for Advanced Science and Technology
Verified email at g.ecc.u-tokyo.ac.jp
Title
Cited by
Cited by
Year
Anomaly detection using autoencoders with nonlinear dimensionality reduction
M Sakurada, T Yairi
Proceedings of the MLSDA 2014 2nd workshop on machine learning for sensory …, 2014
13592014
A review on the application of deep learning in system health management
S Khan, T Yairi
Mechanical Systems and Signal Processing 107, 241-265, 2018
10912018
Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion
N Yokoya, T Yairi, A Iwasaki
IEEE Transactions on Geoscience and Remote Sensing 50 (2), 528-537, 2011
10072011
Learning Koopman invariant subspaces for dynamic mode decomposition
N Takeishi, Y Kawahara, T Yairi
Advances in neural information processing systems 30, 2017
4152017
An approach to spacecraft anomaly detection problem using kernel feature space
R Fujimaki, T Yairi, K Machida
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge …, 2005
3572005
Change-point detection in time-series data based on subspace identification
Y Kawahara, T Yairi, K Machida
Seventh IEEE International Conference on Data Mining (ICDM 2007), 559-564, 2007
1712007
A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction
T Yairi, N Takeishi, T Oda, Y Nakajima, N Nishimura, N Takata
IEEE Transactions on Aerospace and Electronic Systems 53 (3), 1384-1401, 2017
1622017
Fault detection by mining association rules from house-keeping data
T Yairi, Y Kato, K Hori
Proc. of International Symposium on Artificial Intelligence, Robotics and …, 2001
1572001
Telemetry-mining: a machine learning approach to anomaly detection and fault diagnosis for space systems
T Yairi, Y Kawahara, R Fujimaki, Y Sato, K Machida
2nd IEEE International Conference on Space Mission Challenges for …, 2006
1092006
Structured denoising autoencoder for fault detection and analysis
T Tagawa, Y Tadokoro, T Yairi
Asian conference on machine learning, 96-111, 2015
942015
Recent developments in aerial robotics: A survey and prototypes overview
CF Liew, D DeLatte, N Takeishi, T Yairi
arXiv preprint arXiv:1711.10085, 2017
912017
Subspace dynamic mode decomposition for stochastic Koopman analysis
N Takeishi, Y Kawahara, T Yairi
Physical Review E 96 (3), 033310, 2017
812017
Bayesian dynamic mode decomposition.
N Takeishi, Y Kawahara, Y Tabei, T Yairi
IJCAI, 2814-2821, 2017
712017
Automated crater detection algorithms from a machine learning perspective in the convolutional neural network era
DM DeLatte, ST Crites, N Guttenberg, T Yairi
Advances in Space Research 64 (8), 1615-1628, 2019
662019
Unsupervised anomaly detection in unmanned aerial vehicles
S Khan, CF Liew, T Yairi, R McWilliam
Applied Soft Computing 83, 105650, 2019
652019
An anomaly detection method for spacecraft using relevance vector learning
R Fujimaki, T Yairi, K Machida
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 785-790, 2005
622005
Facial expression recognition and analysis: a comparison study of feature descriptors
CF Liew, T Yairi
IPSJ transactions on computer vision and applications 7, 104-120, 2015
552015
Segmentation convolutional neural networks for automatic crater detection on mars
DM DeLatte, ST Crites, N Guttenberg, EJ Tasker, T Yairi
IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2019
512019
Hyperspectral, multispectral, and panchromatic data fusion based on coupled non-negative matrix factorization
N Yokoya, T Yairi, A Iwasaki
2011 3rd workshop on hyperspectral image and signal processing: Evolution in …, 2011
482011
Anomaly detection from multivariate time-series with sparse representation
N Takeishi, T Yairi
2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2014
432014
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