On The Marriage of Lp-norms and Edit Distance

By Lei Chen, Raymond T. Ng. Very Large Data Bases Conference, 2004

862 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9548619cfaf343bc6faab9cd8ae2f0b549a0bc9f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

Deep Representation Learning for Trajectory Similarity Computation (2018, methodology): “ERP [8], EDR [9], DISSIM [16], and the model-driven approach MA [17] were developed to further improve the ability of capturing the spatial semantics in trajectories.”
Time-series clustering - A decade review (2015, methodology): “Penalty (ERP) Robust to noise, shifts and scaling of data, a constant reference point is used Shape-based [134]”
Pattern graph tracking-based stock price prediction using big data (2018, methodology): “There are various methods for pattern matching such as Euclidean distance, Dynamic Time Warping (DTW)([4]), Edit Distance with Real Penalty (ERP)([15]), Longest Common Subsequence (LCSS)([44]) and Edit Distance on Real Sequence (EDR)([16]).”
Trajectory Data Mining (2015, methodology): “ERP is metric, thus can be used to prune unnecessary trajectories [18].”
Rotation-invariant similarity in time series using bag-of-patterns representation (2012, background): “Other elastic distance measures exist for time series, such as Longest Common SubSequence (LCSS) (Vlachos et al. 2002), Edit distance with Real Penalty (ERP) (Chen and Ng 2004) and Time-Warp Edit Distance (TWED) (Marteau 2009).”
Time-series data mining (2012, methodology): “The Edit Distance with Real Penalty (ERP) [Chen and Ng 2004] attempts to combine the merits of DTW and edit distance by using a constant reference point.”
k-Shape: Efficient and Accurate Clustering of Time Series (2015, background): “Instead, starting with the application of DTW decades ago [8], research on that problem has focused on elastic distance measures that compare one-to-many or oneto-none points [11, 12, 44, 55, 78].”
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances (2016, methodology): “For consistency with the published algorithm, window size for DTW is set using cross validation of DTWdistance (rather than CID).”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, methodology): “have been applied to trajectory data include Dynamic Time Warping (DTW) [190], Longest CommonSubsequence (LCSS)[159],and EditDistanceforRealsequences (EDR)[37]andEdit distance with Real Penalty (ERP) [36]. DTW computes the distance based on the sum of minimum distances, instead of choosing the maxim”
Querying and mining of time series data: experimental comparison of representations and distance measures (2008, methodology): “…(ED) [13], Dynamic Time Warping (DTW) [5] [26], distance based on Longest Common Subsequence (LCSS) [39],Edit Distance with Real Penalty (ERP) [8], Edit Distance on Real sequence (EDR) [9], DISSIM [14], Sequence Weighted Alignment model (Swale) [31], Spatial Assembling Distance (SpADe) [12]…”
Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching (2007, methodology): “• Elastic metrics that tolerate time shifting, such as Edit distance with Real Penalty (ERP) [ 5 ].”
k-Shape: Efficient and Accurate Clustering of Time Series (2016, background): “Instead, starting with the application of DTW decades ago [7], research on that problem has focused on elastic distance measures that compare one-to-many or oneto-none points [9, 10, 37, 46, 64].”
Discovery of convoys in trajectory databases (2008, background): “minimized. More recent proposals for trajectory distance functions include Longest Common Subsequence (LCSS) [15], Edit Distance on Real Sequence (EDR) [10], and Edit distance with Real Penalty (ERP) [9]. Lee et al. [20] point out that the above distance measures capture the global similarity betwee”
Using dynamic time warping distances as features for improved time series classification (2016, methodology): “Euclidean distance (ED) (Faloutsos et al. 1994), dynamic time warping (DTW) (Berndt and Clifford 1994), edit distance (Chen and Ng 2004), longest common subsequence (Vlachos et al. 2002) etc. are some examples of distance functions that have been used for time series classification .”