Terminal Dimension Reduction for Time Series with Applications | Paper Citation Record

Observation 1f03761a-d4d4-4abd-b2d8-5ee41babb9c1 · outbound

This paper cites Optimal compression of approximate inner products and dimension reduction.

Terminal Dimension Reduction for Time Series with Applications Optimal compression of approximate inner products and dimension reduction

Reference 1

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Observation 3d6be0ea-0489-4b4c-9299-9ea94484ef22 · outbound

This paper cites Fr \'e chet distance for curves, revisited.

Terminal Dimension Reduction for Time Series with Applications Fr \'e chet distance for curves, revisited

Reference 2

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Observation 7bb55b36-515f-4498-8691-e57a77491ab1 · outbound

This paper cites Sensitivity sampling for k-means: worst case and stability optimal coreset bounds.

Terminal Dimension Reduction for Time Series with Applications Sensitivity sampling for k-means: worst case and stability optimal coreset bounds

Reference 3

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This paper cites Oblivious dimension reduction for k-means: beyond subspaces and the J ohnson- L indenstrauss lemma.

Terminal Dimension Reduction for Time Series with Applications Oblivious dimension reduction for k-means: beyond subspaces and the J ohnson- L indenstrauss lemma

Reference 4

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Terminal Dimension Reduction for Time Series with Applications Unresolved cited work

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Terminal Dimension Reduction for Time Series with Applications Unresolved cited work

Reference 6

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This paper cites Simplified and Improved Bounds on the VC-Dimension for Elastic Distance Measures.

Terminal Dimension Reduction for Time Series with Applications Simplified and Improved Bounds on the VC-Dimension for Elastic Distance Measures

Reference 7

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This paper cites On generalization bounds for projective clustering.

Terminal Dimension Reduction for Time Series with Applications On generalization bounds for projective clustering

Reference 8

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This paper cites Coresets for (k, ) -median clustering under the F r \' e chet distance.

Terminal Dimension Reduction for Time Series with Applications Coresets for (k, ) -median clustering under the F r \' e chet distance

Reference 9

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This paper cites Approximating ( k, \( l \) )-median clustering for polygonal curves.

Terminal Dimension Reduction for Time Series with Applications Approximating ( k, \( l \) )-median clustering for polygonal curves

Reference 10

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This paper cites Augmented functional time series representation and forecasting with gaussian processes.

Terminal Dimension Reduction for Time Series with Applications Augmented functional time series representation and forecasting with gaussian processes

Reference 11

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This paper cites Optimal embedding dimension for sparse subspace embeddings.

Terminal Dimension Reduction for Time Series with Applications Optimal embedding dimension for sparse subspace embeddings

Reference 12

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This paper cites Curve simplification and clustering under fr \' e chet distance.

Terminal Dimension Reduction for Time Series with Applications Curve simplification and clustering under fr \' e chet distance

Reference 13

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Observation 6ef4fd45-fc7c-4d6c-bc31-b424d4ad73fb · outbound

This paper cites Solving F r \' e chet distance problems by algebraic geometric methods.

Terminal Dimension Reduction for Time Series with Applications Solving F r \' e chet distance problems by algebraic geometric methods

Reference 14

Resolution: verified exact; doi, observed 2026-07-13T02:49:20.990734Z

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Observation 8c55a451-117b-45ed-b51e-f38fb1ba99d0 · outbound

This paper cites Terminal embeddings in sublinear time.

Terminal Dimension Reduction for Time Series with Applications Terminal embeddings in sublinear time

Reference 15

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Observation 2f6e7ac3-1fd0-43c0-bc99-ef467411adcb · outbound

This paper cites Clarkson and David P.

Terminal Dimension Reduction for Time Series with Applications Clarkson and David P

Reference 16

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This paper cites Clarkson and David P.

Terminal Dimension Reduction for Time Series with Applications Clarkson and David P

Reference 17

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Terminal Dimension Reduction for Time Series with Applications Unresolved cited work

Reference 18

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Observation 853c0b2c-a031-4cbb-812c-1aa675b42fef · outbound

This paper cites A new coreset framework for clustering.

Terminal Dimension Reduction for Time Series with Applications A new coreset framework for clustering

Reference 19

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Observation ba9fe45b-c09e-4cd6-9d79-35a42dfd9e3c · outbound

This paper cites Towards optimal lower bounds for k -median and k -means coresets.

Terminal Dimension Reduction for Time Series with Applications Towards optimal lower bounds for k -median and k -means coresets

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-13T02:39:19.919959Z

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This paper cites Improved coresets for E uclidean k-means.

Terminal Dimension Reduction for Time Series with Applications Improved coresets for E uclidean k-means

Reference 21

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Observation 344d01d9-7ebd-4238-b28c-b68b8e6e053f · outbound

This paper cites Deterministic clustering in high dimensional spaces: sketches and approximation.

Terminal Dimension Reduction for Time Series with Applications Deterministic clustering in high dimensional spaces: sketches and approximation

Reference 22

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Observation fcced583-7eaf-42d5-ad94-1dac27aa3479 · outbound

This paper cites A tight VC -dimension analysis of clustering coresets with applications.

Terminal Dimension Reduction for Time Series with Applications A tight VC -dimension analysis of clustering coresets with applications

Reference 23

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Observation f6cd4daa-b0c1-4eff-972c-96223385512f · outbound

This paper cites Almost optimal PAC learning for k-means.

Terminal Dimension Reduction for Time Series with Applications Almost optimal PAC learning for k-means

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-13T02:39:19.919959Z

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Observation 81751523-5ba4-4bb1-9f3e-110fc1fda084 · outbound

This paper cites Fast approximations and coresets for (k, ) -median under dynamic time warping.

Terminal Dimension Reduction for Time Series with Applications Fast approximations and coresets for (k, ) -median under dynamic time warping

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-13T02:39:19.919959Z

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Observation 216f0b37-ea6c-4f94-bb6c-03194049d042 · outbound

This paper cites Probabilistic embeddings of the F r \' e chet distance.

Terminal Dimension Reduction for Time Series with Applications Probabilistic embeddings of the F r \' e chet distance

Reference 26

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Observation 76737237-5300-4f70-93e1-41866baad595 · outbound

This paper cites Clustering time series under the F r \' e chet distance.

Terminal Dimension Reduction for Time Series with Applications Clustering time series under the F r \' e chet distance

Reference 27

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This paper cites Phillips, and Ioannis Psarros.

Terminal Dimension Reduction for Time Series with Applications Phillips, and Ioannis Psarros

Reference 28

Resolution: verified exact; doi, observed 2026-07-13T02:49:20.826175Z

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Observation 05243e6b-c95c-42a8-85f5-8ebb15a4b89f · outbound

This paper cites Terminal embeddings.

Terminal Dimension Reduction for Time Series with Applications Terminal embeddings

Reference 29

Resolution: verified exact; doi, observed 2026-07-13T02:49:20.840965Z

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This paper cites Core-sets: An updated survey.

Terminal Dimension Reduction for Time Series with Applications Core-sets: An updated survey

Reference 30

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This paper cites Sparse dimensionality reduction revisited.

Terminal Dimension Reduction for Time Series with Applications Sparse dimensionality reduction revisited

Reference 31

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Terminal Dimension Reduction for Time Series with Applications Unresolved cited work

Reference 32

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Terminal Dimension Reduction for Time Series with Applications Sudhir, and Nisheeth K

Reference 33

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This paper cites Sudhir, and Nisheeth K.

Terminal Dimension Reduction for Time Series with Applications Sudhir, and Nisheeth K

Reference 34

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This paper cites On optimal coreset construction for E uclidean (k, z) -clustering.

Terminal Dimension Reduction for Time Series with Applications On optimal coreset construction for E uclidean (k, z) -clustering

Reference 35

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This paper cites Extensions of Lipschitz mappings into a Hilbert space.

Terminal Dimension Reduction for Time Series with Applications Extensions of Lipschitz mappings into a Hilbert space

Reference 36

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This paper cites Kane and Jelani Nelson.

Terminal Dimension Reduction for Time Series with Applications Kane and Jelani Nelson

Reference 37

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This paper cites Improved learning via k-dtw: A novel dissimilarity measure for curves.

Terminal Dimension Reduction for Time Series with Applications Improved learning via k-dtw: A novel dissimilarity measure for curves

Reference 38

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This paper cites Optimality of the J ohnson- L indenstrauss lemma.

Terminal Dimension Reduction for Time Series with Applications Optimality of the J ohnson- L indenstrauss lemma

Reference 39

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Terminal Dimension Reduction for Time Series with Applications Unresolved cited work

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Observation f7c0d1ad-cfb1-4039-896d-ae6fb15b3f23 · outbound

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Terminal Dimension Reduction for Time Series with Applications Razenshteyn

Reference 41

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Terminal Dimension Reduction for Time Series with Applications Razenshteyn

Reference 42

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This paper cites Exponential and Information Inequalities, chapter 2, pages 15--51.

Terminal Dimension Reduction for Time Series with Applications Exponential and Information Inequalities, chapter 2, pages 15--51

Reference 43

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This paper cites Random projections and sampling algorithms for clustering of high-dimensional polygonal curves.

Terminal Dimension Reduction for Time Series with Applications Random projections and sampling algorithms for clustering of high-dimensional polygonal curves

Reference 44

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Terminal Dimension Reduction for Time Series with Applications Unresolved cited work

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This paper cites Coresets-methods and history: A theoreticians design pattern for approximation and streaming algorithms.

Terminal Dimension Reduction for Time Series with Applications Coresets-methods and history: A theoreticians design pattern for approximation and streaming algorithms

Reference 46

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This paper cites Optimal terminal dimensionality reduction in E uclidean space.

Terminal Dimension Reduction for Time Series with Applications Optimal terminal dimensionality reduction in E uclidean space

Reference 47

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This paper cites Osnap: Faster numerical linear algebra algorithms via sparser subspace embeddings.

Terminal Dimension Reduction for Time Series with Applications Osnap: Faster numerical linear algebra algorithms via sparser subspace embeddings

Reference 48

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This paper cites Bridging the Gap: A Decade Review of Time-Series Clustering Methods.

Terminal Dimension Reduction for Time Series with Applications Bridging the Gap: A Decade Review of Time-Series Clustering Methods

Reference 49

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Terminal Dimension Reduction for Time Series with Applications Unresolved cited work

Reference 50

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Terminal Dimension Reduction for Time Series with Applications Random projections for curves in high dimensions

Reference 51

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Terminal Dimension Reduction for Time Series with Applications Random projections for curves in high dimensions

Reference 52

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This paper cites Every list-decodable code for high noise has abundant near-optimal rate puncturings.

Terminal Dimension Reduction for Time Series with Applications Every list-decodable code for high noise has abundant near-optimal rate puncturings

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-07-13T02:49:20.877257Z

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Observation 59b9b22c-a75c-4d79-9e6a-b17434f6d351 · outbound

This paper cites Improved approximation algorithms for large matrices via random projections.

Terminal Dimension Reduction for Time Series with Applications Improved approximation algorithms for large matrices via random projections

Reference 54

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This paper cites Majorizing measures: The generic chaining.

Terminal Dimension Reduction for Time Series with Applications Majorizing measures: The generic chaining

Reference 55

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Terminal Dimension Reduction for Time Series with Applications Unresolved cited work

Reference 56

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Terminal Dimension Reduction for Time Series with Applications Woodruff

Reference 57

Resolution: unresolved; no resolver link, observed 2026-07-13T02:39:19.919959Z

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This paper cites Anomaly detection in MODIS land products via time series analysis.

Terminal Dimension Reduction for Time Series with Applications Anomaly detection in MODIS land products via time series analysis

Reference 58

Resolution: verified exact; doi, observed 2026-07-13T02:49:20.933220Z

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This paper cites Space complexity of E uclidean clustering.

Terminal Dimension Reduction for Time Series with Applications Space complexity of E uclidean clustering

Reference 59

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Observation 3bc12383-c840-4f4c-871b-1f1dba7d75e5 · outbound

This paper cites Safe active learning for time-series modeling with gaussian processes.

Terminal Dimension Reduction for Time Series with Applications Safe active learning for time-series modeling with gaussian processes

Reference 60

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