在這篇論文中，我們提出了一種新的解決問題的方法，通過分區功能空間近似最近鄰居搜索。隨機典範分區算法可以用來生成二進制代碼的數據，以方便在大型數據集的最近鄰居搜索。根據合奏的分類判別式學習的想法，我們制定一種無監督的學習算法，探索功能空間與隨機選擇的典範。三個大型數據集上的實驗結果表明，我們的方法優於現在尖端的技術，尤其是在較長的二進制代碼的情況下。

In this thesis, we present a new method that addresses the problem of approximate nearest neighbor search via partitioning the feature space. The proposed random exemplar partitioning algorithm can be used to generate binary codes of data to facilitate nearest neighbor search within large datasets. Inspired by the idea of using an ensemble of classifiers for discriminative learning, we devise an unsupervised learning algorithm to explore the feature space with respect to randomly selected exemplars.
Experimental results on three large datasets show that our method outperforms the state-of-the-art, especially on the cases of longer binary codes.

1 Introduction
2 RelatedWork
3 Random Exemplar Partitioning
3.1 Gaussian Normal Affinity
3.2 Single and Dyadic Exemplar Selection Schemes
4 Experiments
4.1 Datasets
4.2 Evaluation details
4.3 Results
4.4 Time Complexity of Querying
5 Conclusion

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