• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.810-831
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• 2019

Recently, continuous dimensional emotion recognition from audiovisual clues has attracted increasing attention in both theory and in practice. The large amount of data involved in the recognition processing decreases the efficiency of most bimodal information fusion algorithms. A novel algorithm, namely the incomplete Cholesky decomposition based kernel cross factor analysis (ICDKCFA), is presented and employed for continuous dimensional audiovisual emotion recognition, in this paper. After the ICDKCFA feature transformation, two basic fusion strategies, namely feature-level fusion and decision-level fusion, are explored to combine the transformed visual and audio features for emotion recognition. Finally, extensive experiments are conducted to evaluate the ICDKCFA approach on the AVEC 2016 Multimodal Affect Recognition Sub-Challenge dataset. The experimental results show that the ICDKCFA method has a higher speed than the original kernel cross factor analysis with the comparable performance. Moreover, the ICDKCFA method achieves a better performance than other common information fusion methods, such as the Canonical correlation analysis, kernel canonical correlation analysis and cross-modal factor analysis based fusion methods.

• 전산구조공학
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• 제1권2호
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• pp.121-130
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• 1988

유한요소 적응분할 해석을 행할 경우 강도매트릭스의 요소배열 형태는 밴드 현상이 아닌 성긴 현상을 갖게 된다. 그러한 성긴 현상의 평형방정식들을 풀기 위해서는 컴퓨터 주기억 장소의 가용량이 중요한 관건이 된다. 따라서, 주기억 장소의 사용량을 최소로 줄이고 수렴속도가 높은 반복법에 의한 해를 구하는 알고리즘이 요구된다. 본 논문에서는 '불완전 Cholesky 분해'를 이용한 선조정 공액구배법을 다른 종류의 선조정 구배법들과 비교, 연구해 본다.