This lecture note describes a technique known as locality-sensitive hashing (LSH) that allows one to quickly find similar entries in large databases. This approach belongs to a novel and interesting class of algorithms that are known as randomized algorithms. A randomized algorithm does not guarantee an exact answer but instead provides a high probability guarantee that it will return the correct answer or one close to it. By investing additional computational effort, the probability can be pushed as high as desired
To compare the similarity of probability distributions, the information-theoretically motivated metr...
We present new methods for computing inter-song similari-ties using intersections between multiple a...
Motivated by the recent interest in stream-ing algorithms for processing large text collections, we ...
We investigate probabilistic hashing techniques for addressing computational and memory challenges i...
Locality Sensitive Hashing (LSH) is widely recognized as one of the most promising approaches to sim...
Locality sensitive hashing (LSH) is a key algorithmic tool that lies at the heart of many informatio...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
The rapidly growing information technology in modern days demands an efficient searching scheme to s...
It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal pro...
Similarity search plays an important role in many applications involving high-dimensional data. Due ...
National audienceLocality Sensitive Hashing (LSH) methods are being successfully employed for scalin...
International audienceIt is well known that high-dimensional nearest-neighbor retrieval is very expe...
Similarity operations on time series are a vital area in data mining research. Science and systems a...
Similarity search plays an important role in many applications involving high-dimensional data. Due ...
To compare the similarity of probability distributions, the information-theoretically motivated metr...
We present new methods for computing inter-song similari-ties using intersections between multiple a...
Motivated by the recent interest in stream-ing algorithms for processing large text collections, we ...
We investigate probabilistic hashing techniques for addressing computational and memory challenges i...
Locality Sensitive Hashing (LSH) is widely recognized as one of the most promising approaches to sim...
Locality sensitive hashing (LSH) is a key algorithmic tool that lies at the heart of many informatio...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
Many modern applications of AI such as web search, mobile browsing, image processing, and natural la...
The rapidly growing information technology in modern days demands an efficient searching scheme to s...
It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal pro...
Similarity search plays an important role in many applications involving high-dimensional data. Due ...
National audienceLocality Sensitive Hashing (LSH) methods are being successfully employed for scalin...
International audienceIt is well known that high-dimensional nearest-neighbor retrieval is very expe...
Similarity operations on time series are a vital area in data mining research. Science and systems a...
Similarity search plays an important role in many applications involving high-dimensional data. Due ...
To compare the similarity of probability distributions, the information-theoretically motivated metr...
We present new methods for computing inter-song similari-ties using intersections between multiple a...
Motivated by the recent interest in stream-ing algorithms for processing large text collections, we ...