37 citations · 77 across the 22 of their papers we have counts for
11 papers · 1 filter
Terminal Dimension Reduction for Time Series with Applications
Alexander Munteanu, Matteo Russo, David Saulpic +1
Terminal embeddings have emerged as a powerful tool for dimension reduction. Given a set of points , a terminal embedding is a mapping $f:\mathbb{R}^d\righta…
Dimension Reduction for Curves: Simplified and Generalized
Matthijs Ebbens, Jie Lu, Alexander Munteanu
We revisit random projections for reducing the dimension of high-dimensional polygonal curves. Drawing from the toolbox of randomized linear algebra, we give a considerably simplif…
Improved Learning via k-DTW: A Novel Dissimilarity Measure for Curves
Amer Krivošija, Alexander Munteanu, André Nusser +1
This paper introduces -Dynamic Time Warping (-DTW), a novel dissimilarity measure for polygonal curves. -DTW has stronger metric properties than Dynamic Time Warping (DTW)…
Turnstile leverage score sampling with applications
Alexander Munteanu, Simon Omlor
The turnstile data stream model offers the most flexible framework where data can be manipulated dynamically, i.e., rows, columns, and even single entries of an input matrix can be…
Optimal bounds for sensitivity sampling via augmentation
Alexander Munteanu, Simon Omlor
Data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of…
Optimal Sketching Bounds for Sparse Linear Regression
Tung Mai, Alexander Munteanu, Cameron Musco +3
We study oblivious sketching for -sparse linear regression under various loss functions such as an norm, or from a broad class of hinge-like loss functions, which inclu…