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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),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.
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.
Alpha-Integration Pooling for Convolutional Neural Networks
Hayoung Eom(엄하영),Heeyoul Choi(최희열) Korean Institute of Information Scientists and Eng 2021 정보과학회논문지 Vol.48 No.7
Convolutional neural networks (CNNs) have achieved remarkable performance in many applications, especially in image recognition tasks. As a crucial component of CNNs, sub-sampling plays an important role for efficient training or invariance property, and max-pooling and arithmetic average-pooling are commonly used sub-sampling methods. In addition to the two pooling methods, however, there are many other pooling types, such as geometric average, harmonic average, among others. Since it is not easy for algorithms to find the best pooling method, usually the pooling types are predefined, which might not be optimal for different tasks. As other parameters in deep learning, however, the type of pooling can be driven by data for a given task. In this paper, we propose α-integration pooling (αI-pooling), which has a trainable parameter α to find the type of pooling. αI-pooling is a general pooling method including max-pooling and arithmetic average-pooling as a special case, depending on the parameter α. Experiments show that αI-pooling outperforms other pooling methods, in image recognition tasks. Also, it turns out that each layer has a different optimal pooling type.