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In many one-class classification problems such as face detection and object verification, the conventional linear discriminant analysis sometimes fails because it makes an inappropriate assumption on negative samples that they are distributed according to a Gaussian distribution. In addition, it sometimes can not extract sufficient number of features(More)
In this paper, we propose a robust principal component analysis (PCA) to overcome the problem that PCA is prone to outliers included in the training set. Different from the other alternatives which commonly replace L2-norm by other distance measures, the proposed method alleviates the negative effect of outliers using the characteristic of the generalized(More)
0167-8655/$ see front matter 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.patrec.2013.06.009 ⇑ Corresponding author. Tel.: +82 31 8040 6332. E-mail addresses: yyongman@ajou.ac.kr (J. Oh), csichoisi@gmail.com (S.-I. Choi), ckim@kitech.re.kr (C. Kim), jungchan.cho@gmail.com (J. Cho), chchoi@snu.ac.kr (C.-H. Choi). 1 Tel.: +82 31 219(More)
Biased discriminant analysis (BDA), which extracts discriminative features for one-class classification problems, is sensitive to outliers in negative samples. This study focuses on the drawback of BDA attributed to the objective function based on the arithmetic mean in one-class classification problems, and proposes an objective function based on a(More)
Sanghyun Kim Dyros Lab, Seoul National University, Suwon, Korea e-mail: ggory15@snu.ac.kr Mingon Kim, Jimin Lee, Soonwook Hwang, Joonbo Chae, Beomyeong Park, Hyunbum Cho, Jaehoon Sim, Jaesug Jung, Hosang Lee, Seho Shin, Minsung Kim, Wonje Choi, Yisoo Lee, and Sumin Park Seoul National University, Suwon, Korea e-mail: mingonkim@snu.ac.kr, jmpechem@snu.ac.kr,(More)
In this paper, we propose a new kernel discriminant analysis using composite vectors (C-KDA). We show that employing composite vectors is similar to using more samples by analysis, which is a great advantage in classification problems when the size of training samples is small. Motivated by this, we apply composite vectors to kernel-based methods, which may(More)
Deep learning has been a growing trend in various fields of natural image classification as it performs state-of-the-art result on several challenging tasks. Despite its success, deep learning applied to medical image analysis has not been wholly explored. In this paper, we study on convolutional neural network (CNN) architectures applied to a Bosniak(More)
In this paper, we propose an effective online method to recognize handwritten music symbols. Based on the fact that most music symbols can be regarded as combinations of several basic strokes, the proposed method first classifies all the strokes comprising an input symbol and then recognizes the symbol based on the results of stroke classification. For(More)
In this paper, we derive a maximum a posteriori (MAP) classifier using the features extracted by biased discriminant analysis (BDA) in multi-class classification problems. Using the one-against-the-rest scheme we construct several feature spaces, where the MAP classifier is formulated. Although the maximum likelihood (ML) classifier is generally equivalent(More)