This is a data-independent approach that relies on a series of hash functions that map similar data points to hash the same location and guarantee that close ...
Locality sensitive hashing scheme based on online-learning ...
dl.acm.org › abs › j.jvcir.2023.104036
Therefore, this paper proposes the OLLSH algorithm based on the Weighted Majority algorithm in the Online-Learning framework, which selects the hash buckets ...
Locality sensitive hashing scheme based on online-learning
www.researchgate.net › publication › 37...
Oct 22, 2024 · Online learning to rank methods aim to optimize ranking models based on user interactions. The dueling bandit gradient descent (DBGD) algorithm ...
Therefore, this paper proposes the OLLSH algorithm based on the Weighted Majority algorithm in the Online-Learning framework, which selects the hash buckets ...
This work proposes a novel semisupervised constrained constrained nonnegative matrix factorization based on label propagation (LpCNMF), which adopts graph ...
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To further boost the practicality of our approach, we develop an online locality-sensitive hashing scheme which leads to efficient up- dates to data structures ...
We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p-stable distributions.
Locality-sensitive hashing (LSH) based frameworks have been used efficiently to select weight vectors in a dense hidden layer with high cosine similarity to ...
Specifically, the LSH clusters similar users into the same bucket, and the fuzzy computing method is developed to predict the types of social relationships ...
M. Ester, H.-P. Kriegel, J. Sander, X. Xu, et al., A density-based algorithm for discovering clusters in large spatial databases with noise, in: Kdd, Vol. 96, ...