On high dimensional two-sample tests based on nearest neighbors

Nearest Neighbors in High-Dimensional Spaces

Nearest Neighbors on Air ?

While the K-Nearest-Neighbor (KNN) problem is well studied in the traditional wired, disk-based client-server environment, it has not been tackled in a wireless broadcast environment. In this paper, the problem of organizing location dependent data and answering KNN queries on air are investigated. The linear property of wireless broadcast media and power conserving requirement of mobile device...

Near-optimal sample compression for nearest neighbors

We present the first sample compression algorithm for nearest neighbors with nontrivial performance guarantees. We complement these guarantees by demonstrating almost matching hardness lower bounds, which show that our bound is nearly optimal. Our result yields new insight into margin-based nearest neighbor classification in metric spaces and allows us to significantly sharpen and simplify exis...

Accelerating High-Dimensional Nearest Neighbors for Video Search

The k-nearest neighbor algorithm (kNN) is a critical algorithm used extensively in fields such as Computer Vision, Robotics, and Machine Learning. In this work, we address the performance of FLANN, a popular kNN library, at the node-level by co-designing indexing and search algorithms with software support. We characterize, profile, and optimize FLANN for high-dimensionality (e.g., ≥ 4096) for ...

Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data

Different aspects of the curse of dimensionality are known to present serious challenges to various machine-learning methods and tasks. This paper explores a new aspect of the dimensionality curse, referred to as hubness, that affects the distribution of k-occurrences: the number of times a point appears among the k nearest neighbors of other points in a data set. Through theoretical and empiri...