Efficient Data Stream Clustering With Sliding Windows Based on Locality-Sensitive Hashing

Locality Sensitive Hashing Based Clustering

Definition In learning systems with kernels, the shape and size of a kernel plays a critical role for accuracy and generalization. Most kernels have a distance metric parameter, which determines the size and shape of the kernel in the sense of a Mahalanobis distance. Advanced kernel learning tune every kernel’s distance metric individually, instead of turning one global distance metric for all ...

Efficient Clustering of Metagenomic Sequences using Locality Sensitive Hashing

The new generation of genomic technologies have allowed researchers to determine the collective DNA of organisms (e.g., microbes) co-existing as communities across the ecosystem (e.g., within the human host). There is a need for the computational approaches to analyze and annotate the large volumes of available sequence data from such microbial communities (metagenomes). In this paper, we devel...

Efficient Incremental Near Duplicate Detection Based on Locality Sensitive Hashing

In this paper, we study the problem of detecting near duplicates for high dimensional data points in an incremental manner. For example, for an image sharing website, it would be a desirable feature if near-duplicates can be detected whenever a user uploads a new image into the website so that the user can take some action such as stopping the upload or reporting an illegal copy. Specifically, ...

Beyond Locality-Sensitive Hashing

We present a new data structure for the c-approximate near neighbor problem (ANN) in the Euclidean space. For n points in R, our algorithm achieves Oc(n + d logn) query time and Oc(n + d logn) space, where ρ ≤ 7/(8c2) + O(1/c3) + oc(1). This is the first improvement over the result by Andoni and Indyk (FOCS 2006) and the first data structure that bypasses a locality-sensitive hashing lower boun...

Locality-Sensitive Hashing