Proficiency-level Estimation Using Heterogeneous Features via Label Dequantized CCA

A proficiency-level estimation method using heterogeneous features by supervised fractional-order embedding multi-view canonical correlation analysis via ordinal label dequantization (SFEMCCA-OLD) is presented in this paper. The proposed method constructs a common latent space by SFEMCCA-OLD, and es...

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Bibliographic Details
Main Authors: Seino, Tatsuki, Saito, Naoki, Ogawa, Takahiro, Asamizu, Satoshi, Haseyama, Miki
Format: Conference Proceeding
Language:English
Subjects:
canonical correlation analysis
Consumer electronics
Correlation
Estimation
ordinal label dequantization
Proficiency-level estimation
Online Access:Request full text
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Summary:A proficiency-level estimation method using heterogeneous features by supervised fractional-order embedding multi-view canonical correlation analysis via ordinal label dequantization (SFEMCCA-OLD) is presented in this paper. The proposed method constructs a common latent space by SFEMCCA-OLD, and estimates proficiency-level using projected features to the common latent space. Then we can utilize new features that have the powerful representation ability since SFEMCCA-OLD enables the construction of the common latent space without dimensionality limitation. Consequently, the performance improvement of the proficiency-level estimation becomes feasible.
ISSN:2575-8284
DOI:10.1109/ICCE-Taiwan58799.2023.10226921