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Unsupervised Hashing Models

Unsupervised Hashing Models offer a fast and efficient way to handle large datasets by partitioning the input feature space using randomly generated hyperplanes—without any dependence on the data’s distribution. This randomness allows for rapid training times, making them the quickest models to train. However, the trade-off is that these models typically require longer hashcodes and multiple hashtables to achieve a sufficient level of retrieval effectiveness.

Below is a collection of key publications related to unsupervised hashing models: