...$\vec{\delta}^{(ij)}=(\delta_{ij1},\ldots,\delta_{ijq})^T$% latex2html id marker 16307
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henceforth, all vectors are taken as column vectors; a row vector can be obtained from the column vector, and vice versa, by transposition, which is denoted with the T superscript
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D2 corresponds to the Euclidean distance, and $D_\infty$ is equivalent to $\displaystyle\max_a\delta_{ija}$
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an $n\times n$ matrix [dij] is said to be Euclidean if n points can be embedded in a Euclidean space of some dimension, such that the Euclidean distance between the ith and jth points is dij, for all $1\le i,j\le n$
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in our work we have used an implementation of PCO - DistPCoA - made publicly available by Philippe Casgrain
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we use the indefinite article because there may be many distinct solutions with the same globally minimal value of the loss function
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... iteration% latex2html id marker 17911
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follows from the fact that the whole dissimilarity matrix $\vec{\Delta}$ of order $n\times n$ has to be inspected at every iteration, where n is the number of objects
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actually a number of workstations have been used, and the timings normalised to that of the 300MHz CPU
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IM is a deterministic algorithm, thus for a given starting configuration it will always converge to the same local minimum
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calculated by expanding the sequence n: $n_1=N^{\displaystyle\alpha^{k-1}}$, and solving $\displaystyle\min_k n_1\le \phi$ for k
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labels correspond to steps of the algorithm
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this is also the worst case running time of standard MDS: O(N3)
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this is also the space requirement of standard MDS: O(N2)
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we have based this evaluation on an implementation of PCA by Fionn Murtagh, available from the StatLib Multivariate Archive
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KYST version 2a is available from Netlib
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the time taken by KYST to generate continuous configurations, which constitute a starting point for the greedy algorithms and SWO, has not been included, as it is negligible when compared to the time required to run GA
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© 2001 Wojciech Basalaj