Question

我正在使用k-means聚类我的数据，但我没有使用标准算法，我使用近似最近邻（ANN）算法来加速样本到中心的比较。这可以通过以下方式轻松完成：

[clusterCenters, trainAssignments] = vl_kmeans(trainDescriptors, clusterCount, 'Algorithm', 'ANN', 'MaxNumComparisons', ceil(clusterCount / 50));

现在，当我运行此代码时，变量' trainDescriptors '被聚类，并且使用ANN将每个描述符分配给' clusterCenters '。

我还有另一个变量' testDescriptors '。我想将它们分配给集群中心。此分配必须使用与“ trainDescriptors ”相同的方法完成，但AFAIK vl_kmeans 函数不会返回它为快速构建的树分配

所以，我的问题是，是否可以将' testDescriptors '分配给' clustersCenters '作为' trainDescriptors '分配给'< em> clusterCenters '在 vl_kmeans 函数中，如果是，我该怎么做？

Answer 1

好吧，我已经明白了。它可以像下面那样完成：

clusterCount = 1024;
datasetTrain = single(rand(128, 100000)); 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1 - cluster train data and get train assignments
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[clusterCenters, trainAssignments_actual] = vl_kmeans(datasetTrain, clusterCount, ...
    'Algorithm', 'ANN', ...
    'Distance', 'l2', ...
    'NumRepetitions', 1, ...
    'NumTrees', 3, ...
    'MaxNumComparisons', ceil(clusterCount / 50) ...
);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 2 - assign train data to clusters centers
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

forest = vl_kdtreebuild(clusterCenters, ...
    'Distance', 'l2', ...
    'NumTrees', 3 ...
);

trainAssignments_expected = vl_kdtreequery(forest, clusterCenters, datasetTrain);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 3 - validate second assignment
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

validation = isequal(trainAssignments_actual, trainAssignments_expected);

在第2步中，我使用群集中心创建新树，然后再次将数据分配给中心。它给出了有效的结果。

使用VLFeat创建集群后，将描述符分配给集群中心

1 个答案: