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Found 5 papers, 0 papers with code
Improving LSH via Tensorized Random Projection
In this work, we aim to propose faster and space efficient locality sensitive hash functions for Euclidean distance and cosine similarity for tensor data.
Improving \textit{Tug-of-War} sketch using Control-Variates method
Tug-of-war} (or AMS) sketch gives a randomized sublinear space (and linear time) algorithm for computing the frequency moments, and the inner product between two frequency vectors corresponding to the data streams.
Dimensionality Reduction for Categorical Data
We show that FSketch is significantly faster, and the accuracy obtained by using its sketches are among the top for the standard unsupervised tasks of RMSE, clustering and similarity search.
Efficient Binary Embedding of Categorical Data using BinSketch
In this work, we present a dimensionality reduction algorithm, aka.
QUINT: Node embedding using network hashing
QUINT is the first of its kind that provides tremendous gain in terms of speed and space usage without compromising much on the accuracy of the downstream tasks.
