Metric MDS #180
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Metric MDS #180
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timholy
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Jun 26, 2022
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| An arbitrary transformation function can be provided to `metric` parameter to | ||
| perform metric MDS with transformed proximities. The function has to accept two parameters, | ||
| a vector of proximities and a vector of distances, in order to calculate disparities |
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What do the vectors represent? I.e., if p is a proximity vector, what is the meaning of p[i]? Likewise, what is the meaning of d[i]?
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Proximity and distance vectors are compact (1D) representations of upper triangular part of proximity and distance matrices correspondingly. I'll add more information to the docs about this.
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| Calculate a symmetric Euclidean (L2) distance matrix. | ||
| """ | ||
| L2distance(X::AbstractMatrix{T}) where {T<:Real} = L2distance!(zeros(T,size(X,2),size(X,2)), X) |
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Presumably we could use Matrix{T}(undef, size(X, 2), size(X, 3)) but the performance gain is unlikely to be observable in practice.
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Implementation of weighted (non)metric MDS with transformed proximities.