In this repository you can find an implementation of LSH (Local | Sensitive Hashing) and Finesse algorithms, designed to find similar data based on their hashes
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Updated
Mar 22, 2024 - C++
In this repository you can find an implementation of LSH (Local | Sensitive Hashing) and Finesse algorithms, designed to find similar data based on their hashes
Locality Sensitive Hashing, fuzzy-hash, min-hash, simhash, aHash, pHash, dHash。基于 Hash值的图片相似度、文本相似度
A Robust Library in C# for Similarity Estimation
distill large scale web page text
Search your object with hash
📈 kNN using LSH and Hypercube projection & Clustering using kMeans++ for n-dim polygonal curves and time series
TTAK.KO-12.0276 LSH Recursive Hasher
📈|Time Series - Nearest neighbor search and Clustering using LSH, Hypercube (and Lloyd's only at the clustering) algorithms with metrics: L2, Discrete and Continuous Fréchet.
Vectors - Nearest neighbor search and Clustering using LSH, Hypercube (and Lloyd's only at the clustering) algorithms with L2 metric.
Implementation tasks for multiple algorithms to process massive data. The algorithms are written in Python.
Image classification and unsupervised learning using latent space vectors produced by convolutional neural nets together with the original vectors space
Image Retrieval implementation using Deep Learning and Kernelized Locality-Sensitive Hashing
This is a task using python to find number of similar songs within the provided songs set.
An implementation of Locality sensitive hashing
Recommendation System on cryptocurrency, using data collected from users' tweets + 10-Fold Cross Validation ( Based on the cryptocoins from each user's tweets, the program runs algorithms on the data, resulting in the recommendation of other cryptocoins for each user) ( readme in greek but soon to be translated in English )
Dataset deduplication using the spark ML lib and Scala
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