語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Simultaneous learning of non-linear ...
~
Boston University.
Simultaneous learning of non-linear manifold and dynamical models for high-dimensional time series.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Simultaneous learning of non-linear manifold and dynamical models for high-dimensional time series.
作者:
Li, Rui.
面頁冊數:
123 p.
附註:
Source: Dissertation Abstracts International, Volume: 70-10, Section: B, page: 6348.
附註:
Adviser: Stan Sclaroff.
Contained By:
Dissertation Abstracts International70-10B.
標題:
Computer Science.
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3382531
ISBN:
9781109435580
Simultaneous learning of non-linear manifold and dynamical models for high-dimensional time series.
Li, Rui.
Simultaneous learning of non-linear manifold and dynamical models for high-dimensional time series.
- 123 p.
Source: Dissertation Abstracts International, Volume: 70-10, Section: B, page: 6348.
Thesis (Ph.D.)--Boston University, 2010.
The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.
ISBN: 9781109435580Subjects--Topical Terms:
212513
Computer Science.
Simultaneous learning of non-linear manifold and dynamical models for high-dimensional time series.
LDR
:02556nmm 2200265 4500
001
280740
005
20110119094942.5
008
110301s2010 ||||||||||||||||| ||eng d
020
$a
9781109435580
035
$a
(UMI)AAI3382531
035
$a
AAI3382531
040
$a
UMI
$c
UMI
100
1
$a
Li, Rui.
$3
492822
245
1 0
$a
Simultaneous learning of non-linear manifold and dynamical models for high-dimensional time series.
300
$a
123 p.
500
$a
Source: Dissertation Abstracts International, Volume: 70-10, Section: B, page: 6348.
500
$a
Adviser: Stan Sclaroff.
502
$a
Thesis (Ph.D.)--Boston University, 2010.
520
$a
The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.
590
$a
School code: 0017.
650
4
$a
Computer Science.
$3
212513
690
$a
0984
710
2
$a
Boston University.
$3
212722
773
0
$t
Dissertation Abstracts International
$g
70-10B.
790
1 0
$a
Sclaroff, Stan,
$e
advisor
790
$a
0017
791
$a
Ph.D.
792
$a
2010
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3382531
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000051889
電子館藏
1圖書
學位論文
TH 2010
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3382531
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入