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Point pattern reconstruction using s...

Zhang, Ying Yuan.

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Point pattern reconstruction using significantly incomplete interpoint distance information: Multidimensional scaling and genetic algorithms approaches.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Point pattern reconstruction using significantly incomplete interpoint distance information: Multidimensional scaling and genetic algorithms approaches./
作者:	Zhang, Ying Yuan.
面頁冊數:	173 p.
附註:	Source: Dissertation Abstracts International, Volume: 59-08, Section: B, page: 4442.
Contained By:	Dissertation Abstracts International59-08B.
標題:	Operations Research. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9900316
ISBN:	0591972239

Point pattern reconstruction using significantly incomplete interpoint distance information: Multidimensional scaling and genetic algorithms approaches.
Zhang, Ying Yuan.

Point pattern reconstruction using significantly incomplete interpoint distance information: Multidimensional scaling and genetic algorithms approaches. - 173 p.

Source: Dissertation Abstracts International, Volume: 59-08, Section: B, page: 4442.

Thesis (Ph.D.)--Tufts University, 1998.

This is a pioneer study of reconstructing a point pattern with significantly incomplete interpoint distances. For the problem in study, we successfully reconstructed and confidently recognized point pattern with as low as 2*N-2 interpoint distances for an N-point pattern. It is slightly above the theoretical GLB of minimum 2*N-3.

ISBN: 0591972239Subjects--Topical Terms:

626629
Operations Research.

Point pattern reconstruction using significantly incomplete interpoint distance information: Multidimensional scaling and genetic algorithms approaches.
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100 1 $a Zhang, Ying Yuan. $3 1925587
245 1 0 $a Point pattern reconstruction using significantly incomplete interpoint distance information: Multidimensional scaling and genetic algorithms approaches.
300 $a 173 p.
500 $a Source: Dissertation Abstracts International, Volume: 59-08, Section: B, page: 4442.
500 $a Adviser: Steven H. Levine.
502 $a Thesis (Ph.D.)--Tufts University, 1998.
520 $a This is a pioneer study of reconstructing a point pattern with significantly incomplete interpoint distances. For the problem in study, we successfully reconstructed and confidently recognized point pattern with as low as 2*N-2 interpoint distances for an N-point pattern. It is slightly above the theoretical GLB of minimum 2*N-3.
520 $a This point pattern reconstruction problem is of NP hard. We deal with it as an optimal search over an unknown multimodal hypersurface. Two optimization routines, multidimensional scaling (MDS) and genetic algorithms (GA's) with different implementation techniques are used in this study for a 10-point two-dimensional pattern with numbers of interpoint distances gradually reduced from complete to theoretical GLB of minimum. Many interesting problems arose as the reconstruction information is reduced. This study shows that the response function corresponding to the incomplete reconstruction information becomes increasingly multimodal; the correlation between partial stress and full stress decreases; the difference between the satisfactory reconstruction in image recognition sense and the mathematical "near optimal solution" increase.
520 $a Representation of point patterns by interpoint distances has solid practical background. It is preferable to coordinate representation under many circumstances. Point patterns are of interest in a number of contexts. Star constellations and molecular essentially consist of points. Complex shapes are often approximated by a relatively small number of "feature points". Many problems benefit from representing a pattern with as little information as is practical. These include issues of computer usage, reducing data storage and computational requirements, the time and cost of data acquisition and, in psychology experiments, the additional consideration of the resistance of human subjects to excessive testing.
520 $a This study also finds that: GA's do explore greater region in search than MDS and efficiently converge to lots of near-optimal solutions. The important issue is to distinguish the global minimum from other local minima when low amounts of information are available. Multiple searches (MDS or GA's) can significantly improve the ability of search as well as recognize the global minimum solution. Hybrid techniques with GA's and MDS not only improving the efficiency of GA's but also greatly avoid GA's "premature convergence". There is no simple relationship between near optimal solution (in the mathematical sense) and "satisfactory solution" (in image recognition sense). What a subject judges to be a satisfactory is not only dependent on the value of partial stress but also related to the distribution of the partial stress. This study indicates that among different solutions with similar partial stress, the one with even stress distribution have the higher probability of being satisfactory solutions.
590 $a School code: 0234.
650 4 $a Operations Research. $3 626629
650 4 $a Engineering, System Science. $3 1018128
650 4 $a Computer Science. $3 626642
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710 2 0 $a Tufts University. $3 1017847
773 0 $t Dissertation Abstracts International $g 59-08B.
790 1 0 $a Levine, Steven H., $e advisor
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791 $a Ph.D.
792 $a 1998
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