On the Unsurprising Behaviour of Kernels in High Dimensions

Kaur, Avneet and Raj, Harsh and Jayaram, Balasubramaniam (2020) On the Unsurprising Behaviour of Kernels in High Dimensions. In: IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020, 30-31 October 2020, Greater Noida; India.

Full text not available from this repository. (Request a copy)


Kernels are employed in ML algorithms not only as a means of measuring similarity but also for their interesting theoretical interpretations and computational advantages. Among them Gaussian kernels have been extensively employed for their ease of interpretation and locality. However, it has been observed [4] that they lose many of their advantages in high dimensions. Hence a suitable modification of it has been proposed to overcome this loss. In this work, we firstly show that despite these modifications not all the lost advantages have been recovered and leads us to consider alternate kernels. However, we contend that either a change or a modification of an underlying kernel only treats the symptoms and discuss what could be the main malaise.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Jayaram, Balasubramaniamhttp://orcid.org/0000-0001-7370-3821
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Discriminating Power, Gaussian Kernel, High dimensional data analysis, Triangular Kernel
Subjects: Mathematics
Mathematics > General principles of mathematics
Mathematics > Algebra
Mathematics > Arithmetics
Mathematics > Topology
Mathematics > Geometry
Mathematics > Numerical analysis
Mathematics > Probabilities and applied mathematics
Divisions: Department of Mathematics
Depositing User: . LibTrainee 2021
Date Deposited: 27 May 2021 07:45
Last Modified: 27 May 2021 07:45
URI: http://raiith.iith.ac.in/id/eprint/7847
Publisher URL: http://doi.org/10.1109/ICCCA49541.2020.9250782
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 7847 Statistics for this ePrint Item