Terminal Dimension Reduction for Time Series with Applications
Terminal embeddings have emerged as a powerful tool for dimension reduction. Given a set of points $P\subset \mathbb{R}^d$, a terminal embedding is a mapping $f:\mathbb{R}^d\rightarrow \mathbb{R}^t$ that preserves the pairwise distance between any pair of points $p\in P$ and $q\in \mathbb{R}^d$ up to small distortion under this mapping. Terminal embeddings have been particularly fruitful for constructing $k$-means and $k$-median coresets, where the objective is to find a typically weighted subset $Ω$ of $P$ such that for any candidate solution, the cost of the clustering objective on $Ω$ appro
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via arxiv author · 85%Alexander Munteanu →
“Terminal Dimension Reduction for Time Series with Applications”
- LinkedLinked via arxiv author · 85%Matteo Russo →
“Terminal Dimension Reduction for Time Series with Applications”
- LinkedLinked via arxiv author · 85%David Saulpic →
“Terminal Dimension Reduction for Time Series with Applications”
- LinkedLinked via arxiv author · 85%Chris Schwiegelshohn →
“Terminal Dimension Reduction for Time Series with Applications”
- FuzzySimilar title/name (fuzzy) · 59%Fincept-Corporation/FinceptTerminal →
“Fuzzy title match (0.73): “Terminal Dimension Reduction for Time Series with Applicatio” ≈ “Fincept-Corporation/FinceptTerminal””