Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T14:00:10.920853Z
Paper Citation Record · LEDGER
Anomaly Detection in High Dimensional Data
As of 16 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:1908.04000.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
29 of 29 outbound references displayed
- verified exact0
- verified fuzzy20
- unresolved8
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation caafb0af-2353-4226-9acc-fdc65cf8b913 · outbound
This paper cites an unresolved cited work.
Anomaly Detection in High Dimensional Data Unresolved cited work
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T14:00:11.497735Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7750a3fd-04c4-40b9-9997-ecf3be1c0954 · outbound
This paper cites Burridge, P & AMR Taylor (2006).
Anomaly Detection in High Dimensional Data Burridge, P & AMR Taylor (2006)
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.056366Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e3578278-1d3d-4fd4-9bad-6fb7c5602547 · outbound
This paper cites Anomaly Detection in High Dimensional Data Talagala, Hyndman, Smith-Miles: 12 August 2019 17 The HDoutliers algorithm has two versions.
Anomaly Detection in High Dimensional Data Anomaly Detection in High Dimensional Data Talagala, Hyndman, Smith-Miles: 12 August 2019 17 The HDoutliers algorithm has two versions
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.252487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8728b1e3-b450-4288-8eff-cf88f55f7560 · outbound
This paper cites Anomalies are represented by red triangles and black dots correspond to the typical behaviour.
Anomaly Detection in High Dimensional Data Anomalies are represented by red triangles and black dots correspond to the typical behaviour
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.389340Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2db6fa90-428d-4d68-aa8e-7d914e16d565 · outbound
This paper cites This point corresponds to the sensor positions at Southbank in Melbourne where New Year’s Eve fireworks attract millions of spectators annually.
Anomaly Detection in High Dimensional Data This point corresponds to the sensor positions at Southbank in Melbourne where New Year’s Eve fireworks attract millions of spectators annually
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.133505Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4f05ec63-ce44-4c4e-aca2-05438244f701 · outbound
This paper cites The focus is then to detect anomalous instances (rows) in the dataset.
Anomaly Detection in High Dimensional Data The focus is then to detect anomalous instances (rows) in the dataset
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.334319Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1e1fe5bd-31fb-4588-ba57-e7f58876a802 · outbound
This paper cites an unresolved cited work.
Anomaly Detection in High Dimensional Data Unresolved cited work
Reference 11
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T14:00:11.318227Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0c9477fb-3e3f-412c-828b-cf73a6430c46 · outbound
This paper cites The anomalous threshold calculation in Schwarz (2008); Burridge & Taylor (2006) and Wilkinson (2017) is an application of Weissman’s spacing theorem (Weissman.
Anomaly Detection in High Dimensional Data The anomalous threshold calculation in Schwarz (2008); Burridge & Taylor (2006) and Wilkinson (2017) is an application of Weissman’s spacing theorem (Weissman
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.301608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4c02e359-c3cb-48e9-870a-0022be985360 · outbound
This paper cites an unresolved cited work.
Anomaly Detection in High Dimensional Data Unresolved cited work
Reference 14
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T14:00:11.268948Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c842232f-ce7e-4d59-b2ca-8d081ca7be00 · outbound
This paper cites That is, for a small cluster to be a micro cluster, the number of data points in that cluster should be less than k.
Anomaly Detection in High Dimensional Data That is, for a small cluster to be a micro cluster, the number of data points in that cluster should be less than k
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.208241Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1588b49e-b61a-464c-8843-c2d60157b365 · outbound
This paper cites Each scatterplot follows a negatively skewed distribution.
Anomaly Detection in High Dimensional Data Each scatterplot follows a negatively skewed distribution
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.193128Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8c2cdcc9-fca9-44f1-b781-48c21ca698c8 · outbound
This paper cites Both days, 1 December 2018 and 31 December 2018, display an unusual rise later in the day.
Anomaly Detection in High Dimensional Data Both days, 1 December 2018 and 31 December 2018, display an unusual rise later in the day
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.175011Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 10e03953-91ba-4e8b-875a-2777981027c5 · outbound
This paper cites an unresolved cited work.
Anomaly Detection in High Dimensional Data Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T14:00:11.160345Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 25face70-b14e-45df-ad9c-cd77ce637b8d · outbound
This paper cites It also can be used to identify anomalous time series within a large collection of streaming temporal data.
Anomaly Detection in High Dimensional Data It also can be used to identify anomalous time series within a large collection of streaming temporal data
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.108375Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8f01ccce-a660-4028-99e7-892f54b3f13d · outbound
This paper cites In addition to a label, the stray algorithm also assigns an anomalous score to each data instance to indicate the degree of outlierness of each measurement.
Anomaly Detection in High Dimensional Data In addition to a label, the stray algorithm also assigns an anomalous score to each data instance to indicate the degree of outlierness of each measurement
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.092483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 79365f64-3832-4671-8242-bb448d67abd9 · outbound
This paper cites A feature-based framework for detecting technical outliers in water-quality data from in situ sensors.
Anomaly Detection in High Dimensional Data A feature-based framework for detecting technical outliers in water-quality data from in situ sensors
Reference 209
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-14T14:00:10.982848Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f6ec4b1e-701e-4b57-8297-a3a18673560a · outbound
This paper cites A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data.
Anomaly Detection in High Dimensional Data A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data
Reference 283
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.015634Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 674a3537-e25f-4f13-8275-152dbaa1c562 · outbound
This paper cites In the Leader algorithm, each cluster is a ball in the high-dimensional data space.
Anomaly Detection in High Dimensional Data In the Leader algorithm, each cluster is a ball in the high-dimensional data space
Reference 1975
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.369855Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 02e724ec-4196-4204-be3c-822fb8860ec1 · outbound
This paper cites an unresolved cited work.
Anomaly Detection in High Dimensional Data Unresolved cited work
Reference 1978
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T14:00:11.284377Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fd76e8f1-a6df-498e-a88c-ec6956fa2e76 · outbound
This paper cites Gao, J, W Hu, ZM Zhang, X Zhang & O Wu (2011).
Anomaly Detection in High Dimensional Data Gao, J, W Hu, ZM Zhang, X Zhang & O Wu (2011)
Reference 1993
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.038524Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5d51a625-3941-4a12-b419-ab1008f432e7 · outbound
This paper cites This could be due to the parallelisability and memory access patterns of the two searching mechanisms.
Anomaly Detection in High Dimensional Data This could be due to the parallelisability and memory access patterns of the two searching mechanisms
Reference 2002
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.233504Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 95e16929-24ae-4727-bd00-9b1a3a25e126 · outbound
This paper cites Local parametric density-based outlier detection and ensemble learning with applications to malware detection.
Anomaly Detection in High Dimensional Data Local parametric density-based outlier detection and ensemble learning with applications to malware detection
Reference 2005
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:10.999953Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7ea4b4a5-aca3-4a54-965f-4c9a7baf4a05 · outbound
This paper cites Some algorithms are application specific and take advantage of the underlying data structure or other domain-specific knowledge (Talagala et al.
Anomaly Detection in High Dimensional Data Some algorithms are application specific and take advantage of the underlying data structure or other domain-specific knowledge (Talagala et al
Reference 2008
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.435042Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fe5063b2-b557-4f36-83dd-550ada757fc9 · outbound
This paper cites Outlier detection.
Anomaly Detection in High Dimensional Data Outlier detection
Reference 2014
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.073642Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cfda831c-6540-4603-a5d3-b622058f5cfb · outbound
This paper cites an unresolved cited work.
Anomaly Detection in High Dimensional Data Unresolved cited work
Reference 2015
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T14:00:11.484467Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 26ed8894-0e37-40cc-b1c1-2d704136f023 · outbound
This paper cites an unresolved cited work.
Anomaly Detection in High Dimensional Data Unresolved cited work
Reference 2016
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T14:00:11.470956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 866ad653-87a5-4975-bf39-ed772fe19940 · outbound
This paper cites 2014), machine learning and statistical domains (Hodge & Austin 2004), novelty detection (Pimentel et al.
Anomaly Detection in High Dimensional Data 2014), machine learning and statistical domains (Hodge & Austin 2004), novelty detection (Pimentel et al
Reference 2017
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.455390Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ccbc0f99-d7a1-488e-83c0-716455754e7d · outbound
This paper cites an unresolved cited work.
Anomaly Detection in High Dimensional Data Unresolved cited work
Reference 2018
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T14:00:11.351853Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 899da23b-f6c0-464f-9fde-acd2e08e9e31 · outbound
This paper cites Third, we demonstrate the wide applicability and usefulness of our stray algorithm, using various datasets.
Anomaly Detection in High Dimensional Data Third, we demonstrate the wide applicability and usefulness of our stray algorithm, using various datasets
Reference 2019
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T14:00:11.414027Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.