Favorite Video Estimation Based on Multiview Feature Integration via KMvLFDA

Favorite Video Estimation Based on Multiview Feature Integration via KMvLFDA

This paper presents a novel method for favorite video estimation based on multiview feature integration via kernel multiview local fisher discriminant analysis (KMvLFDA). The proposed method first extracts electroencephalogram (EEG) features from users' EEG signals recorded while watching video...

Full description

Bibliographic Details
Main Authors: Akira Toyoda, Takahiro Ogawa, Miki Haseyama
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Canonical correlation analysis
electroencephalogram (EEG)
Fisher discriminant analysis
individual preference
Online Access:https://ieeexplore.ieee.org/document/8494731/

Internet

https://ieeexplore.ieee.org/document/8494731/

Similar Items