- 'kdtree' — Creates and uses a Kd-tree to find nearest neighbors.
- 'exhaustive' — Uses the exhaustive search algorithm. When predicting the class of a new point xnew, the algorithm computes the distance values from all points in X to xnew to find nearest neighbors.
what is the 'NSMethod','exhaustive' k-nearest neighbor classifier
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Kong on 11 Apr 2020
Commented: Kong on 17 Apr 2020
I am using the k-nearest neighbor classifier as the below code.
Could you explain what 'NSMethod','exhaustive' are?
Is it related to distance metric learning? (It learns a metric that pulls the neighbor candidates near, while pushes near data from different classes out of the target neighbors margin.) I got a high accuracy than I expected.
for i = 2:30:750
X = csvread('kth_optical_only.csv');
Y = csvread('kth_optical_only_class1.csv');
X = X(:,1:i);
Mdl = fitcknn(X,Y,'NumNeighbors',3,...
rng(1); % For reproducibility
CVKNNMdl = crossval(Mdl, 'KFold', 5);
classAccuracy(i) = 100 - kfoldLoss(CVKNNMdl, 'LossFun', 'ClassifError')*100;
Divya Gaddipati on 13 Apr 2020
NSMethod is the Nearest neighbor search method, specified as either 'kdtree' or 'exhaustive'.
For information on exhaustive and kdtree based searching, you can refer to the following links:
Hope this helps!
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