5 accepted papers
In *expand-and-sparsify* (EaS) representation, a data point in $\mathcal{S}^{d-1}$ is first randomly mapped to higher dimension $\mathbb{R}^m$, where $m>d$, followed by a sparsification operation where the informative $k \ll m$ of the $m$ coordinates are set to one and the rest are set to zero. We p…
The mathematical formalization of a neurological mechanism in the fruit-fly olfactory circuit as a locality sensitive hash (Flyhash) and bloom filter (FBF) has been recently proposed and "reprogrammed" for various learning tasks such as similarity search, outlier detection and text embeddings. We pr…
Nearest neighbor search using random projection trees has recently been shown to achieve superior performance, in terms of better accuracy while retrieving less number of data points, compared to locality sensitive hashing based methods. However, to achieve acceptable nearest neighbor search accurac…
K-means clustering algorithm using Lloyd’s heuristic is one of the most commonly used tools in data mining and machine learning that shows promising performance. However, it suffers from a high computational cost resulting from pairwise Euclidean distance computations between data points and cluster…
Randomized partition trees have recently been shown to be very effective in solving nearest neighbor search problem. In spite of enjoying strong theoretical guarantee, it suffers from high space complexity, since each internal node of the tree needs to store a $d$ dimensional projection direction le…