Performance of Minkowskitype Distances in Similarity Search  A Geometrical Approach
Singh, Arpan and Jayaram, Balasubramaniam (2020) Performance of Minkowskitype Distances in Similarity Search  A Geometrical Approach. In: 5th International Conference on Computing Communication and Automation, ICCCA, 3031 October 2020, Greater Noida; India.
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This work is an attempt at exploring distances, in the context of Similarity Search (SS), where an approximate match for a given query q is sought from a given dataset $\mathcal{X}$. One view is that the query q itself is a noise η corrupted version of an $x\in \mathcal{X}$. Recently, François et al., [1] had studied the efficacy of Minkowskitype distances in retrieving the x given q in the presence of both white and highly coloured noise η. Noting that not all conclusions in [1] hold true, in high dimensions, in this work, we have undertaken a similar study but that which differs in the following way: Taking into account various other factors not considered in [1]. Our geometric approach to these investigations have revealed hitherto unknown impact of both the domain geometry and denseness of the data set and has led us to propose an index which AIDS in explaining the simulation results obtained and in understanding the impact of the 3D's of Dimensionality, Domain geometry and Denseness of the data on the appropriateness of a Distance function in the setting of SS algorithms.
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Item Type:  Conference or Workshop Item (Paper)  
Uncontrolled Keywords:  Euclidean, Fractional and Minkowski distances, High dimensional data analysis,Relative Contained VolumeSimilarity Search  
Subjects:  Mathematics Mathematics > General principles of mathematics Mathematics > Algebra Mathematics > Arithmetics Mathematics > Geometry Mathematics > Numerical analysis Mathematics > Probabilities and applied mathematics 

Divisions:  Department of Mathematics  
Depositing User:  . LibTrainee 2021  
Date Deposited:  27 May 2021 07:29  
Last Modified:  27 May 2021 07:29  
URI:  http://raiith.iith.ac.in/id/eprint/7846  
Publisher URL:  http://doi.org/10.1109/ICCCA49541.2020.9250751  
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