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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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
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creator Seino, Tatsuki
Saito, Naoki
Ogawa, Takahiro
Asamizu, Satoshi
Haseyama, Miki
description 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.
doi_str_mv 10.1109/ICCE-Taiwan58799.2023.10226921
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subjects canonical correlation analysis
Consumer electronics
Correlation
Estimation
ordinal label dequantization
Proficiency-level estimation
title Proficiency-level Estimation Using Heterogeneous Features via Label Dequantized CCA
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