Abstract:
Classification is challenged by data collection frequency in some applications. We study mixed frequency data as a special type of multi-view data with different sampling frequencies for each view. DPLOL-MF is a discriminative dictionary pair learning method for mixed frequency data classification constrained by ordinal locality. This method integrates synthesis and analysis dictionaries into a dictionary pair to reduce computational cost caused by the l0 or l1-norm constraint and address sampling frequency inconsistency. The DPLOL-MF uses synthesis and analysis dictionaries to learn class-specified reconstruction information and generate coding coefficients from samples. The ordinal locality preserving term constrains dictionary pair atoms to make the learned dictionary pair more discriminative. We also create a mixed frequency data classification scheme for inconsistent sample sizes. This paper presents a novel approach to classifying mixed frequency data, and the results show its efficacy.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
