K Nearest Neighbour with different distance matrix to each datapoint Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsWeighted k nearest neighbor searchName of this algorithm for supervised cluster assignmentAgglomerative Hierarchial Clustering in python using DTW distancek-Nearest Neighbours with time series data - how to obtain whole-time-period estimatorsHow to get nearest 5 points mean with Nearest Neighbours?How can I apply PCA to KNN?1 - Nearest Neighbor , dealing with same distanceIs there any way of ordering/sorting vectors?How can I implement tangent distance for k-nearest neighbor in python/scikit-learn?Fast way of computing covariance matrix of nonstationary kernel in Python
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K Nearest Neighbour with different distance matrix to each datapoint
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWeighted k nearest neighbor searchName of this algorithm for supervised cluster assignmentAgglomerative Hierarchial Clustering in python using DTW distancek-Nearest Neighbours with time series data - how to obtain whole-time-period estimatorsHow to get nearest 5 points mean with Nearest Neighbours?How can I apply PCA to KNN?1 - Nearest Neighbor , dealing with same distanceIs there any way of ordering/sorting vectors?How can I implement tangent distance for k-nearest neighbor in python/scikit-learn?Fast way of computing covariance matrix of nonstationary kernel in Python
$begingroup$
I'm wondering if there is library support in python (such as sklearn) for doing KNN on a data set that has a custom distance matrix (positive definite) for each data point (x is a query point, $x_i$ is a data set point):
$$
d(x,x_i) = (x-x_i)^TQ_i(x-x_i)
$$
I know that for a fixed positive definite matrix for all data points, this is a metric that I can transform into
$$
Q = A^TA
d(x,x_i) = (Ax - Ax_i)^T(Ax - Ax_i)
$$
Which I can compute via normal KNN by first transforming the input space via multiplying $A$.
My problem of having a separate matrix for each data point came up because I have a covariance around the neighbourhood of each point. KNN can then be interpreted as what are the most likely neighbourhoods this query point lies in. If a neighbourhood doesn't vary along a dimension then we should penalize difference along that dimension highly in terms of increasing distance.
machine-learning scikit-learn distance k-nn
asked 16 mins ago
LemonPiLemonPi
101
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LemonPi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
I'm wondering if there is library support in python (such as sklearn) for doing KNN on a data set that has a custom distance matrix (positive definite) for each data point (x is a query point, $x_i$ is a data set point):
$$
d(x,x_i) = (x-x_i)^TQ_i(x-x_i)
$$
I know that for a fixed positive definite matrix for all data points, this is a metric that I can transform into
$$
Q = A^TA
d(x,x_i) = (Ax - Ax_i)^T(Ax - Ax_i)
$$
Which I can compute via normal KNN by first transforming the input space via multiplying $A$.
My problem of having a separate matrix for each data point came up because I have a covariance around the neighbourhood of each point. KNN can then be interpreted as what are the most likely neighbourhoods this query point lies in. If a neighbourhood doesn't vary along a dimension then we should penalize difference along that dimension highly in terms of increasing distance.
machine-learning scikit-learn distance k-nn
asked 16 mins ago
LemonPiLemonPi
101
New contributor
LemonPi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I'm wondering if there is library support in python (such as sklearn) for doing KNN on a data set that has a custom distance matrix (positive definite) for each data point (x is a query point, $x_i$ is a data set point):
$$
d(x,x_i) = (x-x_i)^TQ_i(x-x_i)
$$
I know that for a fixed positive definite matrix for all data points, this is a metric that I can transform into
$$
Q = A^TA
d(x,x_i) = (Ax - Ax_i)^T(Ax - Ax_i)
$$
Which I can compute via normal KNN by first transforming the input space via multiplying $A$.
My problem of having a separate matrix for each data point came up because I have a covariance around the neighbourhood of each point. KNN can then be interpreted as what are the most likely neighbourhoods this query point lies in. If a neighbourhood doesn't vary along a dimension then we should penalize difference along that dimension highly in terms of increasing distance.
machine-learning scikit-learn distance k-nn
asked 16 mins ago
LemonPiLemonPi
101
New contributor
LemonPi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm wondering if there is library support in python (such as sklearn) for doing KNN on a data set that has a custom distance matrix (positive definite) for each data point (x is a query point, $x_i$ is a data set point):
$$
d(x,x_i) = (x-x_i)^TQ_i(x-x_i)
$$
I know that for a fixed positive definite matrix for all data points, this is a metric that I can transform into
$$
Q = A^TA
d(x,x_i) = (Ax - Ax_i)^T(Ax - Ax_i)
$$
Which I can compute via normal KNN by first transforming the input space via multiplying $A$.
My problem of having a separate matrix for each data point came up because I have a covariance around the neighbourhood of each point. KNN can then be interpreted as what are the most likely neighbourhoods this query point lies in. If a neighbourhood doesn't vary along a dimension then we should penalize difference along that dimension highly in terms of increasing distance.
machine-learning scikit-learn distance k-nn
machine-learning scikit-learn distance k-nn
asked 16 mins ago
LemonPiLemonPi
101
New contributor
LemonPi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 16 mins ago
LemonPiLemonPi
101
New contributor
LemonPi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 16 mins ago
LemonPiLemonPi
101
New contributor
LemonPi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 16 mins ago
LemonPiLemonPi
101
asked 16 mins ago
LemonPiLemonPi
101
101
New contributor
LemonPi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
LemonPi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
LemonPi is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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