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Reference를 갖는 ICA를 이용한 자동적 P300 검출
최희열(Heeyoul Choi),최승진(Seungjin Choi) 한국정보과학회 2003 한국정보과학회 학술발표논문집 Vol.30 No.1B
The analysis of EEG data is an important task in the domain of Brain Computer Interface (BCI). In general, this task is extremely difficult because EEG data is very noisy and contains many artifacts and consists of mixtures of several brain waves. The P300 component of the evoked potential is a relatively evident signal which has a large positive wave that occurs around 300 msec after a task-relevant stimulus. Thus automatic detection of P300 is useful in BCI. To this end, in this paper we employ a method of reference-based independent component analysis (ICA) which overcomes the ordering ambiguity in the conventional ICA. We show here that ICA incorporating with prior knowledge is useful in the task of automatic P300 detection.
최희열(Heeyoul Choi),최승진(Seungjin Choi) 한국정보과학회 2005 한국정보과학회 학술발표논문집 Vol.32 No.1
Isomap [1] is a manifold learning algorithm, which extends classical multidimensional scaling (MDS) by considering approximate geodesic distance instead of Euclidean distance. The approximate geodesic distance matrix can be interpreted as a kernel matrix, which implies that Isomap can be solved by a kernel eigenvalue problem. However, the geodesic distance kernel matrix is not guaranteed to be positive semidefinite. In this paper we employ a constant-adding method, which leads to the Mercer kernel-based Isomap algorithm. Numerical experimental results with noisy "Swiss roll" data, confirm the validity and high performance of our kernel Isomap algorithm.
최희열(Heeyoul Choi),김숙정(Sookjeong Kim),최승진(Seungjin Choi) 한국정보과학회 2004 한국정보과학회 학술발표논문집 Vol.31 No.1B
A trust-region method is a quite attractive optimization technique. It is, in general, faster than the steepest descent method and is free of a learning rate unlike the gradient-based methods. In addition to its convergence property (between linear and quadratic convergence), its stability is always guaranteed, in contrast to the Newton's method. In this paper, we present an efficient implementation of the maximum likelihood independent component analysis (ICA) using the trustregion method, which leads to trust-region-based ICA (TR-ICA) algorithms. The useful behavior of our TR-ICA algorithms is confirmed through numerical experimental results.
Pet Shop Recommendation System based on Implicit Feedback
Heeyoul Choi(최희열),Yunhee Kang(강윤희),Myungju Kang(강명주) 한국디지털콘텐츠학회 2017 한국디지털콘텐츠학회논문지 Vol.18 No.8
Due to the advances in machine learning and artificial intelligence technologies, many new services have become available. Among such services, recommendation systems have already been successfully applied to commercial services and made profits as in online shopping malls. Most recommendation algorithms in commercial services are based on content analysis or explicit feedback rates as in movie recommendations. However, many online shopping malls have difficulties in content analysis or are lacking explicit feedbacks on their items, which results in no recommendation system for their items. Even for such service systems, user log data is easily available, and if recommendations are possible with such log data, the quality of their service can be improved. In this paper, we extract implicit feedback like click information for items from log data and provide a recommendation system based on the implicit feedback. The proposed system is applied to a real in-service online shopping mall.
EdNet 데이터 기반 학습자의 지식 수준 예측을 위한 딥러닝 모델 개선
최슬기(Seulgi Choi),김영표(Youngpyo Kim),황소정(Sojung Hwang),최희열(Heeyoul Choi) Korean Institute of Information Scientists and Eng 2021 정보과학회논문지 Vol.48 No.12
As online education increases, the field of AI in Education (AIEd), where artificial intelligence is used for education, is being actively studied. Knowledge Tracing (KT), which predicts a students knowledge level based on each students learning record, is a basic task in the AIEd field. However, there is a lack of utilization of the dataset and research on the KT model architecture. In this paper, we propose to use a total of 11 features, after trying various features related to the problems, and present a new model based on the self-attention mechanism with new query, key, and values, Self-Attentive Knowledge Tracking Extended (SANTE). In experiments, we confirm that the proposed method with the selected features outperforms the previous KT models in terms of the AUC value.
Jeonghyun Joo(주정현),Heeyoul Choi(최희열) 한국정보과학회 2021 정보과학회 컴퓨팅의 실제 논문지 Vol.27 No.4
딥러닝 프레임워크는 컴퓨터 비전 많은 분야에서 괄목할 만한 성과를 보여주고 있다. 비디오 행동 인식 분야 역시 딥러닝 모델을 적용하기 위한 많은 연구들이 수행되었다. 한 선행연구는 2차원 CNN을 이용해 공간적 피쳐를 학습하고 이를 RNN에 입력으로 전달해 이용해 공간적 피쳐 사이의 시간적 상호 관계를 학습하는 모델 구조를 제안했다. 본 논문에서는 RNN 대신 1차원 CNN을 이용해 시간적 상호관계를 학습하도록 선행 연구의 모델 구조를 개선하는 연구를 수행한다. 이러한 구조 변경을 통해 RNN의 순차적 연산 과정을 제거해 향상된 GPU 활용도를 기대할 수 있다. 본 논문은 수정된 모델이 정확도를 비슷하게 유지하면서 연산 시간이 줄어드는 것을 보여주는 실험 결과를 제시함으로써 이러한 주장을 뒷받침한다. The deep learning framework has shown remarkable results on numerous computer vision tasks. Many studies have been performed for video action recognition tasks to apply deep learning models to the task. One of the previous works suggested the model architecture, where spatial features are learned from 2D Convolutional Neural Networks (CNNs) and then passed to Recurrent Neural Networks (RNNs) to learn about temporal dependency among them. In this paper, we study the improved model architecture where the temporal relationship of spatial features is processed with 1D CNN instead of RNN. From this modification, we can expect better utilization of GPU by removing sequential operations of RNN. We support the argument based on the experiment results that show that it leads to the reduction in computation time and maintains a similar classification accuracy.