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Found 13 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.
Improved Outlier Robust Seeding for k-means
However, in the presence of adversarial noise or outliers, $D^{2}$ sampling is more likely to pick centers from distant outliers instead of inlier clusters, and therefore its approximation guarantees \textit{w. r. t.}
Minwise-Independent Permutations with Insertion and Deletion of Features
We note that a naive solution to this problem is to repeatedly recompute $\mathrm{minHash}$ with respect to the updated dimension.
One-pass additive-error subset selection for $\ell_{p}$ subspace approximation
In this paper, we give a one-pass subset selection with an additive approximation guarantee for $\ell_{p}$ subspace approximation, for any $p \in [1, \infty)$.
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.
On Subspace Approximation and Subset Selection in Fewer Passes by MCMC Sampling
Our ideas also extend to give a reduction in the number of passes required by adaptive sampling algorithms for $\ell_{p}$ subspace approximation and subset selection, for $p \geq 2$.
IHashNet: Iris Hashing Network based on efficient multi-index hashing
Here, in this paper, we propose an iris indexing scheme using real-valued deep iris features binarized to iris bar codes (IBC) compatible with the indexing structure.
Subspace approximation with outliers
Any multiplicative approximation algorithm for the subspace approximation problem with outliers must solve the robust subspace recovery problem, a special case in which the $(1-\alpha)n$ inliers in the optimal solution are promised to lie exactly on a $k$-dimensional linear subspace.
Efficient Sketching Algorithm for Sparse Binary Data
We compare the performance of our algorithm with the state-of-the-art algorithms on the task of mean-square-error and ranking.
Efficient Compression Technique for Sparse Sets
Ideally, the compressed representation of the data should be such, that the similarity between each pair of data points is preserved, while keeping the time and the randomness required for compression as low as possible.
