Create a multidimensional points cloud with given only the distance between the points












0












$begingroup$


as the title says, I am trying to get the most accurate multidimensional points cloud with the leas dimensions needed given only the distance between all the points. The dimension of the solution is not given either.


e.g. I have given three points A, B and C:

Points AB have the Distance 1

Points AC have the Distance 1

Points BC have the Distance 1.41 (sqrt(2))



In this simple case, the best solution would be 2 dimensional like this:



A – – – C

| – – – |

| – – – |

| – – – |

B – – – .



As there are more points and more dimension, this problem gets more complicated. The input distance between the points will be empirical values and will therefore never give a unique solution.

But I think the best solution could be narrowed down, this would be the solution with the least error.

I guess there must already be an approach to this problem, but I did not yet find any solution. Do you have any suggestions?


Thank you a lot!










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$endgroup$












  • $begingroup$
    This is the classical multidimensional scaling problem. See e.g. sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/…
    $endgroup$
    – Rahul
    Dec 18 '18 at 12:40


















0












$begingroup$


as the title says, I am trying to get the most accurate multidimensional points cloud with the leas dimensions needed given only the distance between all the points. The dimension of the solution is not given either.


e.g. I have given three points A, B and C:

Points AB have the Distance 1

Points AC have the Distance 1

Points BC have the Distance 1.41 (sqrt(2))



In this simple case, the best solution would be 2 dimensional like this:



A – – – C

| – – – |

| – – – |

| – – – |

B – – – .



As there are more points and more dimension, this problem gets more complicated. The input distance between the points will be empirical values and will therefore never give a unique solution.

But I think the best solution could be narrowed down, this would be the solution with the least error.

I guess there must already be an approach to this problem, but I did not yet find any solution. Do you have any suggestions?


Thank you a lot!










share|cite|improve this question









$endgroup$












  • $begingroup$
    This is the classical multidimensional scaling problem. See e.g. sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/…
    $endgroup$
    – Rahul
    Dec 18 '18 at 12:40
















0












0








0





$begingroup$


as the title says, I am trying to get the most accurate multidimensional points cloud with the leas dimensions needed given only the distance between all the points. The dimension of the solution is not given either.


e.g. I have given three points A, B and C:

Points AB have the Distance 1

Points AC have the Distance 1

Points BC have the Distance 1.41 (sqrt(2))



In this simple case, the best solution would be 2 dimensional like this:



A – – – C

| – – – |

| – – – |

| – – – |

B – – – .



As there are more points and more dimension, this problem gets more complicated. The input distance between the points will be empirical values and will therefore never give a unique solution.

But I think the best solution could be narrowed down, this would be the solution with the least error.

I guess there must already be an approach to this problem, but I did not yet find any solution. Do you have any suggestions?


Thank you a lot!










share|cite|improve this question









$endgroup$




as the title says, I am trying to get the most accurate multidimensional points cloud with the leas dimensions needed given only the distance between all the points. The dimension of the solution is not given either.


e.g. I have given three points A, B and C:

Points AB have the Distance 1

Points AC have the Distance 1

Points BC have the Distance 1.41 (sqrt(2))



In this simple case, the best solution would be 2 dimensional like this:



A – – – C

| – – – |

| – – – |

| – – – |

B – – – .



As there are more points and more dimension, this problem gets more complicated. The input distance between the points will be empirical values and will therefore never give a unique solution.

But I think the best solution could be narrowed down, this would be the solution with the least error.

I guess there must already be an approach to this problem, but I did not yet find any solution. Do you have any suggestions?


Thank you a lot!







geometry algebraic-geometry






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asked Dec 18 '18 at 10:01









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  • $begingroup$
    This is the classical multidimensional scaling problem. See e.g. sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/…
    $endgroup$
    – Rahul
    Dec 18 '18 at 12:40




















  • $begingroup$
    This is the classical multidimensional scaling problem. See e.g. sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/…
    $endgroup$
    – Rahul
    Dec 18 '18 at 12:40


















$begingroup$
This is the classical multidimensional scaling problem. See e.g. sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/…
$endgroup$
– Rahul
Dec 18 '18 at 12:40






$begingroup$
This is the classical multidimensional scaling problem. See e.g. sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/…
$endgroup$
– Rahul
Dec 18 '18 at 12:40












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