國立虎尾科技大學 |

Multi-view Representation Learning with Applications to Functional Neuroimaging Data.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Multi-view Representation Learning with Applications to Functional Neuroimaging Data./
作者:	Chen, Po-Hsuan.
面頁冊數:	1 online resource (144 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355322927

Multi-view Representation Learning with Applications to Functional Neuroimaging Data.
Chen, Po-Hsuan.

Multi-view Representation Learning with Applications to Functional Neuroimaging Data. - 1 online resource (144 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

One of the greatest challenges for the 21st century is understanding how the human brain works. Although there are different levels of understanding of the human brain, a key step is knowing how brain activity patterns map onto cognition, emotion, memories, etc. This can be studied using functional magnetic resonance imaging (fMRI). fMRI is a non-invasive brain imaging technique with unprecedented spatiotemporal resolution. The fMRI data is gathered while subjects perform a wide-range of cognitive tasks. Analysis of fMRI data using multivariate statistics and machine learning has led to tremendous success in understanding how patterns of neural activity reflect mental representations. This thesis aims to continue the success through advancing machine learning methods motivated by applications to neuroscience problems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355322927Subjects--Topical Terms:

561152
Engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Multi-view Representation Learning with Applications to Functional Neuroimaging Data.
LDR:03474ntm a2200385Ki 4500 001 910499
005 20180517123956.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9780355322927
035 $a (MiAaPQ)AAI10601138
035 $a (MiAaPQ)princeton:12239
035 $a AAI10601138
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Chen, Po-Hsuan. $3 1181825
245 1 0 $a Multi-view Representation Learning with Applications to Functional Neuroimaging Data.
264 0 $c 2017
300 $a 1 online resource (144 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Adviser: Peter J. Ramadge.
502 $a Thesis (Ph.D.) $c Princeton University $d 2017.
504 $a Includes bibliographical references
520 $a One of the greatest challenges for the 21st century is understanding how the human brain works. Although there are different levels of understanding of the human brain, a key step is knowing how brain activity patterns map onto cognition, emotion, memories, etc. This can be studied using functional magnetic resonance imaging (fMRI). fMRI is a non-invasive brain imaging technique with unprecedented spatiotemporal resolution. The fMRI data is gathered while subjects perform a wide-range of cognitive tasks. Analysis of fMRI data using multivariate statistics and machine learning has led to tremendous success in understanding how patterns of neural activity reflect mental representations. This thesis aims to continue the success through advancing machine learning methods motivated by applications to neuroscience problems.
520 $a We develop a multi-view learning framework that estimates shared features from multi-view data. We analyze and demonstrate two primary approaches of how can a multi-view learning framework provide new ways of exploring neuroimaging data. First, a multi-view learning model forms a larger dataset by aggregating data from multiple views. A key potential advantage of this is an increase in statistical sensitivity. Second, a multi-view learning model learns a shared feature space and transformations between each view's observation space and the shared feature space. These transformations bridge any two views, opening up new possibilities for analyzing the data. For example, by treating a subject as a view, we can transform one subject's fMRI data into the space of another subject's brain. By treating semantic vectors of stimulus text description and fMRI response as different views, it opens up the opportunity to generate text from fMRI responses or fMRI responses from text.
520 $a Lastly, we explore various forms of multi-view learning models, including manifold learning, probabilistic modeling, deep neural network, etc. Different ways of applying multi-view models on neuroimaging data are demonstrated and analyzed. We also discuss our contribution to the open-source software community.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Engineering. $3 561152
650 4 $a Computer science. $3 573171
650 4 $a Neurosciences. $3 593561
655 7 $a Electronic books. $2 local $3 554714
690 $a 0537
690 $a 0984
690 $a 0317
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Princeton University. $b Electrical Engineering. $3 1181685
773 0 $t Dissertation Abstracts International $g 79-04B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10601138 $z click for full text (PQDT)