I joined the Electrotechnical Laboratory, AIST, MITI in 1981. From 1990 to 1991, I was a visiting research scientist at Institute for Information Technology, National Research Council Canada. From 2001 to 2009, I was a deputy director of Nueroscience Research Institute, National Institute of Advanced Industrial Science and Technology (AIST). Also, I was a Professor at the Graduate School of Systems and Information Engineering, University of Tsukuba, from 2002 to 2009. I am currently a Professor at Hiroshima University. My current research interests include statistical pattern recognition and its applications to image recognition.
For dimensionality reduction, t-distributed stochastic neighbor embedding (t-SNE) is famous. This... more For dimensionality reduction, t-distributed stochastic neighbor embedding (t-SNE) is famous. This technique represents the similarity between the pair of the samples in the high-dimensional space as Gaussian distribution. Then the similarity between the pair of the samples in the low-dimensional embedding space is also represented by using t-distribution to obtain the feature vectors in the embedding space from the high-dimensional data. The authors proposed q-Gaussian distributed stochastic neighbor embedding (q-SNE) as an extension of the t-SNE. The q-Gaussian distribution can express many distributions by setting hyperparameter q$= and includes the Gaussian distribution and the t-distribution as the special cases with hyperparameter q close to 1.0 and q = 2.0. However, these methods are applicable for a given data set and it is not possible to map new samples into the embedded space. To address this problem, the parametric t-SNE is proposed to construct the non-linear mapping by using a feed-forward neural network. In this paper, we propose a novel technique called parametric q-SNE with Convolutional Neural Network without pre-training. On MNIST, FashionMNIST, and COIL-20, the effectiveness of the parametric q -SNE is shown by using the visualization on 2-dimensional mapping, and the classification by using k nearest neighbors (k-NN) on the embedded space.
Abstract In this study, we propose a novel autoencoder framework based on orthogonal projection c... more Abstract In this study, we propose a novel autoencoder framework based on orthogonal projection constraint (OPC) for anomaly detection (AD) on both complex image and vector datasets. Orthogonal projection is useful to capture the null subspace that consists of noisy information for AD, which is explicitly ignored in the existing approaches. The exploration of double subspaces, called normal space (NS) and abnormal space (AS) can improve the discriminative manifold information. Therefore, in this study, autoencoder framework based on the OPC learning method is proposed that combines the orthogonal subspace score and the reconstruction error score in the target tasks for AD. To the best of our knowledge, this is the first study that introduces an autoencoder-based model with two orthogonal subspaces for AD. Through the orthogonality, the anomaly-free data and abnormal ⧹ nosiy information are projected into the NS and the AS, respectively. Thus, it potentially addresses the problem of the distribution of generative model by combining the abilities of two subspaces that can appropriately learn the features and establish a strict boundaries around the normal data. For image datasets, we propose a convolutional autoencoder based on OPC. Additionally, the generalization and adaptability of the proposed method in AD was investigated using vector datasets by implementing a fully-connected layer-based OPC in the encoder-decoder structure. The effectiveness of the proposed framework for AD was evaluated through the comparison with state-of-the-art approaches.
For dimensionality reduction, t-distributed stochastic neighbor embedding (t-SNE) is famous. This... more For dimensionality reduction, t-distributed stochastic neighbor embedding (t-SNE) is famous. This technique represents the similarity between the pair of the samples in the high-dimensional space as Gaussian distribution. Then the similarity between the pair of the samples in the low-dimensional embedding space is also represented by using t-distribution to obtain the feature vectors in the embedding space from the high-dimensional data. The authors proposed q-Gaussian distributed stochastic neighbor embedding (q-SNE) as an extension of the t-SNE. The q-Gaussian distribution can express many distributions by setting hyperparameter q$= and includes the Gaussian distribution and the t-distribution as the special cases with hyperparameter q close to 1.0 and q = 2.0. However, these methods are applicable for a given data set and it is not possible to map new samples into the embedded space. To address this problem, the parametric t-SNE is proposed to construct the non-linear mapping by using a feed-forward neural network. In this paper, we propose a novel technique called parametric q-SNE with Convolutional Neural Network without pre-training. On MNIST, FashionMNIST, and COIL-20, the effectiveness of the parametric q -SNE is shown by using the visualization on 2-dimensional mapping, and the classification by using k nearest neighbors (k-NN) on the embedded space.
Abstract In this study, we propose a novel autoencoder framework based on orthogonal projection c... more Abstract In this study, we propose a novel autoencoder framework based on orthogonal projection constraint (OPC) for anomaly detection (AD) on both complex image and vector datasets. Orthogonal projection is useful to capture the null subspace that consists of noisy information for AD, which is explicitly ignored in the existing approaches. The exploration of double subspaces, called normal space (NS) and abnormal space (AS) can improve the discriminative manifold information. Therefore, in this study, autoencoder framework based on the OPC learning method is proposed that combines the orthogonal subspace score and the reconstruction error score in the target tasks for AD. To the best of our knowledge, this is the first study that introduces an autoencoder-based model with two orthogonal subspaces for AD. Through the orthogonality, the anomaly-free data and abnormal ⧹ nosiy information are projected into the NS and the AS, respectively. Thus, it potentially addresses the problem of the distribution of generative model by combining the abilities of two subspaces that can appropriately learn the features and establish a strict boundaries around the normal data. For image datasets, we propose a convolutional autoencoder based on OPC. Additionally, the generalization and adaptability of the proposed method in AD was investigated using vector datasets by implementing a fully-connected layer-based OPC in the encoder-decoder structure. The effectiveness of the proposed framework for AD was evaluated through the comparison with state-of-the-art approaches.
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