.png){kind=icon}
.png){kind=icon}
Return
Enhancing Vector Comparison Using HMMs
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3312019.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Vectors are massively used in many domains. Several techniques have been proposed for comparing two vectors, but they only perform the comparison according to the exact values of the vector components. Additionally, existing techniques used for comparing two vectors having different dimensions are limited by many factors. Furthermore, the problem of comparing two finite sets of vectors has not yet been specifically addressed. This paper attempts to overcome all these limitations by proposing a new technique based on hidden Markov models which enhances existing techniques by giving them the ability to compare two finite sets of vectors, each containing vectors having different dimensions, while precising the set of targeted properties on which the comparison should be performed. Classification experiments conducted on three online available custom datasets demonstrated that when the suitable set of targeted properties is selected, the proposed approach outperforms existing techniques with accuracy gains reaching +82.3\% .
Keywords:
Hidden Markov models
Automobiles
Computer science
Task analysis
Earth
Correlation coefficient
Vectors
Distance measurement
vector comparison
distance between vectors
hidden Markov models
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W