Representation of k-nearest neighbour algorithms
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I have a dataset distributed in n-dimensional space. Each datum has a weight. I am applying a k-nearest-neighbour-type algorithm that is sensitive not to the number of nearest neighbours but to the sum weight of nearest neighbours: so at each point I am calculating a weighted mean of a number of neighbouring points such that the sum weight = k.
How would I represent this? Or is it best just to describe it in prose?
pattern-recognition
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up vote
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down vote
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I have a dataset distributed in n-dimensional space. Each datum has a weight. I am applying a k-nearest-neighbour-type algorithm that is sensitive not to the number of nearest neighbours but to the sum weight of nearest neighbours: so at each point I am calculating a weighted mean of a number of neighbouring points such that the sum weight = k.
How would I represent this? Or is it best just to describe it in prose?
pattern-recognition
add a comment |Â
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I have a dataset distributed in n-dimensional space. Each datum has a weight. I am applying a k-nearest-neighbour-type algorithm that is sensitive not to the number of nearest neighbours but to the sum weight of nearest neighbours: so at each point I am calculating a weighted mean of a number of neighbouring points such that the sum weight = k.
How would I represent this? Or is it best just to describe it in prose?
pattern-recognition
I have a dataset distributed in n-dimensional space. Each datum has a weight. I am applying a k-nearest-neighbour-type algorithm that is sensitive not to the number of nearest neighbours but to the sum weight of nearest neighbours: so at each point I am calculating a weighted mean of a number of neighbouring points such that the sum weight = k.
How would I represent this? Or is it best just to describe it in prose?
pattern-recognition
asked Jul 23 at 13:25
tzirtzi
11
11
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