A Multilabel Classification Framework for Approximate Nearest Neighbor Search

To learn partition-based index structures for approximate nearest neighbor (ANN) search, both supervised and unsupervised machine learning algorithms have been used. Existing supervised algorithms select all the points that belong to the same partition element as the query point as nearest neighbor candidates. Consequently, they formulate the learning task as finding a partition in which the nearest neighbors of a query point belong to the same partition element with it as often as possible. In contrast, we formulate the candidate …

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