Hierarchical Latent Dictionaries for Models of Brain Activation

Alona Fyshe, Emily Fox, David Dunson, Tom Mitchell
; Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:409-421, 2012.

Abstract

In this work, we propose a hierarchical latent dictionary approach to estimate the time-varying mean and covariance of a process for which we have only limited noisy samples. We fully leverage the limited sample size and redundancy in sensor measurements by transferring knowledge through a hierarchy of lower dimensional latent processes. As a case study, we utilize Magnetoencephalography (MEG) recordings of brain activity to identify the word being viewed by a human subject. Specifically, we identify the word category for a single noisy MEG recording, when given only limited noisy samples on which to train.

Cite this Paper


BibTeX
@InProceedings{pmlr-v22-fyshe12, title = {Hierarchical Latent Dictionaries for Models of Brain Activation}, author = {Alona Fyshe and Emily Fox and David Dunson and Tom Mitchell}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {409--421}, year = {2012}, editor = {Neil D. Lawrence and Mark Girolami}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/fyshe12/fyshe12.pdf}, url = {http://proceedings.mlr.press/v22/fyshe12.html}, abstract = {In this work, we propose a hierarchical latent dictionary approach to estimate the time-varying mean and covariance of a process for which we have only limited noisy samples. We fully leverage the limited sample size and redundancy in sensor measurements by transferring knowledge through a hierarchy of lower dimensional latent processes. As a case study, we utilize Magnetoencephalography (MEG) recordings of brain activity to identify the word being viewed by a human subject. Specifically, we identify the word category for a single noisy MEG recording, when given only limited noisy samples on which to train.} }
Endnote
%0 Conference Paper %T Hierarchical Latent Dictionaries for Models of Brain Activation %A Alona Fyshe %A Emily Fox %A David Dunson %A Tom Mitchell %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-fyshe12 %I PMLR %J Proceedings of Machine Learning Research %P 409--421 %U http://proceedings.mlr.press %V 22 %W PMLR %X In this work, we propose a hierarchical latent dictionary approach to estimate the time-varying mean and covariance of a process for which we have only limited noisy samples. We fully leverage the limited sample size and redundancy in sensor measurements by transferring knowledge through a hierarchy of lower dimensional latent processes. As a case study, we utilize Magnetoencephalography (MEG) recordings of brain activity to identify the word being viewed by a human subject. Specifically, we identify the word category for a single noisy MEG recording, when given only limited noisy samples on which to train.
RIS
TY - CPAPER TI - Hierarchical Latent Dictionaries for Models of Brain Activation AU - Alona Fyshe AU - Emily Fox AU - David Dunson AU - Tom Mitchell BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics PY - 2012/03/21 DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-fyshe12 PB - PMLR SP - 409 DP - PMLR EP - 421 L1 - http://proceedings.mlr.press/v22/fyshe12/fyshe12.pdf UR - http://proceedings.mlr.press/v22/fyshe12.html AB - In this work, we propose a hierarchical latent dictionary approach to estimate the time-varying mean and covariance of a process for which we have only limited noisy samples. We fully leverage the limited sample size and redundancy in sensor measurements by transferring knowledge through a hierarchy of lower dimensional latent processes. As a case study, we utilize Magnetoencephalography (MEG) recordings of brain activity to identify the word being viewed by a human subject. Specifically, we identify the word category for a single noisy MEG recording, when given only limited noisy samples on which to train. ER -
APA
Fyshe, A., Fox, E., Dunson, D. & Mitchell, T.. (2012). Hierarchical Latent Dictionaries for Models of Brain Activation. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in PMLR 22:409-421

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