國立虎尾科技大學 |

Principal Network Analysis.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Principal Network Analysis./
作者:	Mei, Jonathan B.
面頁冊數:	1 online resource (154 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355966565

Principal Network Analysis.
Mei, Jonathan B.

Principal Network Analysis. - 1 online resource (154 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2018.

Includes bibliographical references

Many applications collect a large number of time series, for example, temperature continuously monitored by weather stations across the US or neural activity recorded by an array of electrical probes. These data are often referred to as unstructured. A first task in their analytics is often to derive a low dimensional representation -- a graph or discrete manifold -- that describes the interrelations among the time series and their intra relations across time.

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

Mode of access: World Wide Web

ISBN: 9780355966565Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Principal Network Analysis.
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245 1 0 $a Principal Network Analysis.
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300 $a 1 online resource (154 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 $a Adviser: Jose M.F. Moura.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2018.
504 $a Includes bibliographical references
520 $a Many applications collect a large number of time series, for example, temperature continuously monitored by weather stations across the US or neural activity recorded by an array of electrical probes. These data are often referred to as unstructured. A first task in their analytics is often to derive a low dimensional representation -- a graph or discrete manifold -- that describes the interrelations among the time series and their intra relations across time.
520 $a In general, the underlying graphs can be directed and weighted, possibly capturing the strengths of causal relations, not just the binary existence of reciprocal correlations. Furthermore, the processes generating the data may be non-linear and observed in the presence of unmodeled phenomena or unmeasured agents in a complex networked system. Finally, the networks describing the processes may themselves vary through time.
520 $a In many scenarios, there may be good reasons to believe that the graphs are only able to vary as linear combinations of a set of "principal graphs" that are fundamental to the system. We would then be able to characterize each principal network individually to make sense of the ensemble and analyze the behaviors of the interacting entities.
520 $a This thesis acts as a roadmap of computationally tractable approaches for learning graphs that provide structure to data. It culminates in a framework that addresses these challenges when estimating time-varying graphs from collections of time series. Analyses are carried out to justify the various models proposed along the way and to characterize their performance. Experiments are performed on synthetic and real datasets to highlight their effectiveness and to illustrate their limitations.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Computer science. $3 573171
650 4 $a Statistics. $3 556824
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710 2 $a Carnegie Mellon University. $b Electrical and Computer Engineering. $3 1182305
773 0 $t Dissertation Abstracts International $g 79-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10822110 $z click for full text (PQDT)