Motoaki Kawanabe

Publications of Motoaki Kawanabe

• Stationary common spatial patterns for brain-computer interfacing.

  Authors: Wojciech Samek, Carmen Vidaurre, Klaus-Robert Müller, Motoaki Kawanabe

  Journal of neural engineering. 02/2012; 9(2):026013.

  Classifying motion intentions in brain-computer interfacing (BCI) is a demanding task as the recorded EEG signal is not only noisy and has limited spatial resolution but it is also intrinsicallyClassifying motion intentions in brain-computer interfacing (BCI) is a demanding task as the recorded EEG signal is not only noisy and has limited spatial resolution but it is also intrinsically non-stationary. The non-stationarities in the signal may come from many different sources, for instance, electrode artefacts, muscular activity or changes of task involvement, and often deteriorate classification performance. This is mainly because features extracted by standard methods like common spatial patterns (CSP) are not invariant to variations of the signal properties, thus should also change over time. Although many extensions of CSP were proposed to, for example, reduce the sensitivity to noise or incorporate information from other subjects, none of them tackles the non-stationarity problem directly. In this paper, we propose a method which regularizes CSP towards stationary subspaces (sCSP) and show that this increases classification accuracy, especially for subjects who are hardly able to control a BCI. We compare our method with the state-of-the-art approaches on different datasets, show competitive results and analyse the reasons for the improvement.

• Insights from Classifying Visual Concepts with Multiple Kernel Learning

  Authors: Alexander Binder, Shinichi Nakajima, Marius Kloft, Christina Müller, Wojciech Samek, Ulf Brefeld, Klaus-Robert Müller, Motoaki Kawanabe

  12/2011;

  Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown inCombining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, so-called 1-norm MKL variants are often observed to be outperformed by an unweighted sum kernel. The contribution of this paper is twofold: We apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks within computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum kernel SVM and the sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. About to be submitted to PLoS ONE.

• Multi-task Learning via Non-sparse Multiple Kernel Learning

  Authors: Wojciech Samek, Alexander Binder, Motoaki Kawanabe

  01/2011: pages 335-342;

  ISBN: 978-3-642-23671-6

• Uniqueness of Non-Gaussianity-Based Dimension Reduction.

  Authors: Fabian J. Theis, Motoaki Kawanabe, Klaus-Robert Müller

  IEEE Transactions on Signal Processing. 01/2011; 59:4478-4482.

• An Information Geometrical View of Stationary Subspace Analysis.

  Authors: Motoaki Kawanabe, Wojciech Samek, Paul von Bünau, Frank C. Meinecke

  Artificial Neural Networks and Machine Learning - ICANN 2011 - 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II; 01/2011

• An Information Geometrical View of Stationary Subspace Analysis

  Authors: Motoaki Kawanabe, Wojciech Samek, Paul Bünau von, Frank Meinecke

  01/2011: pages 397-404;

  ISBN: 978-3-642-21737-1

• Stationary Common Spatial Patterns: Towards Robust Classification of Non-Stationary EEG Signals

  Authors: Wojciech Wojcikiewicz, Carmen Vidaurre, Motoaki Kawanabe

  Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on; 01/2011

  Brain-Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as acquired, e.g., by EEG. A standard step in a BCI system is to project the EEG signals to aBrain-Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as acquired, e.g., by EEG. A standard step in a BCI system is to project the EEG signals to a low-dimensional subspace using Common Spatial Patterns (CSP). However, non-stationarities in the data can negatively affect the performance of CSP, i.e. variation of the signal properties within and across experimental sessions coming from electrode artefacts, alpha or muscular activity, or fatigue may result in suboptimal projection directions. We alleviate this problem by regularizing CSP towards stationary subspaces and show that this especially increases classification accuracy of people who are not able to control a BCI i.e. have more than 30% of error. These users very often show non-stationarities in their EEG signals.

• Multi-modal Visual Concept Classification of Images via Markov Random Walk over Tags

  Authors: Motoaki Kawanabe, Alexander Binder, Christina Müller, Wojciech Wojcikiewicz

  Applications of Computer Vision (WACV), 2011 IEEE Workshop on; 01/2011

  Automatic annotation of images is a challenging task in computer vision because of “semantic gap” between high-level visual concepts and image appearances. Therefore, user tags attached to images canAutomatic annotation of images is a challenging task in computer vision because of “semantic gap” between high-level visual concepts and image appearances. Therefore, user tags attached to images can provide further information to bridge the gap, even though they are partially uninformative and misleading. In this work, we investigate multi-modal visual concept classification based on visual features and user tags via kernel-based classifiers. An issue here is how to construct kernels between sets of tags. We deploy Markov random walks on graphs of key tags to incorporate co-occurrence between them. This procedure acts as a smoothing of tag based features. Our experimental result on the ImageCLEF2010 PhotoAnnotation benchmark shows that our proposed method outperforms the baseline relying solely on visual information and a recently published state-of-the-art approach.

• Improving Classification Performance of BCIs by Using Stationary Common Spatial Patterns and Unsupervised Bias Adaptation.

  Authors: Wojciech Wojcikiewicz, Carmen Vidaurre, Motoaki Kawanabe

  Hybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Wroclaw, Poland, May 23-25, 2011, Proceedings, Part II; 01/2011

• Improving Classification Performance of BCIs by using Stationary Common Spatial Patterns and Unsupervised Bias Adaptation

  Authors: Wojciech Wojcikiewicz, Carmen Vidaurre, Motoaki Kawanabe

  01/2011: pages 34-41;

  ISBN: 978-3-642-21221-5

• Modelling Non-stationarities in EEG Data with Robust Principal Component Analysis.

  Authors: Javier Pascual, Motoaki Kawanabe, Carmen Vidaurre

  Hybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Wroclaw, Poland, May 23-25, 2011, Proceedings, Part II; 01/2011

• A Hybrid Supervised-Unsupervised Vocabulary Generation Algorithm for Visual Concept Recognition

  Authors: Alexander Binder, Wojciech Wojcikiewicz, Christina Müller, Motoaki Kawanabe

  01/2011: pages 95-108;

• The Joint Submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the ImageCLEF2011 Photo Annotation Task

  Authors: Alexander Binder, Wojciech Samek, Marius Kloft, Christina Müller, Klaus-Robert Müller, Motoaki Kawanabe

  CLEF (Notebook Papers/Labs/Workshop); 01/2011

• An Information Geometrical View of Stationary Subspace Analysis

  Authors: Motoaki Kawanabe, Wojciech Samek, Paul Bünau von, Frank Meinecke

  Artificial Neural Networks and Machine Learning - ICANN 2011; 01/2011

• Group-wise Stationary Subspace Analysis - A Novel Method for Studying Non-Stationarities

  Authors: Wojciech Samek, Motoaki Kawanabe, Carmen Vidaurre

  01/2011: pages 16-20;

  ISBN: 978-3-85125-140-1

• Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search.

  Authors: Masashi Sugiyama, Makoto Yamada, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe

  Neural networks : the official journal of the International Neural Network Society. 10/2010; 24(2):183-98.

  Methods for directly estimating the ratio of two probability density functions have been actively explored recently since they can be used for various data processing tasks such as non-stationarityMethods for directly estimating the ratio of two probability density functions have been actively explored recently since they can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, and feature selection. In this paper, we develop a new method which incorporates dimensionality reduction into a direct density-ratio estimation procedure. Our key idea is to find a low-dimensional subspace in which densities are significantly different and perform density-ratio estimation only in this subspace. The proposed method, D(3)-LHSS (Direct Density-ratio estimation with Dimensionality reduction via Least-squares Hetero-distributional Subspace Search), is shown to overcome the limitation of baseline methods.

• Direct Density Ratio Estimation with Dimensionality Reduction.

  Authors: Masashi Sugiyama, Satoshi Hara, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe

  Proceedings of the SIAM International Conference on Data Mining, SDM 2010, April 29 - May 1, 2010, Columbus, Ohio, USA; 01/2010

• Shrinking Large Visual Vocabularies using Multi-label Agglomerative Information Bottleneck

  Authors: Wojciech Wojcikiewicz, Alexander Binder, Motoaki Kawanabe

  Image Processing (ICIP), 2010 17th IEEE International Conference on; 01/2010

  The quality of visual vocabularies is crucial for the performance of bag-of-words image classification methods. Several approaches have been developed for codebook construction, the most popularThe quality of visual vocabularies is crucial for the performance of bag-of-words image classification methods. Several approaches have been developed for codebook construction, the most popular method is to cluster a set of image features (e.g. SIFT) by k-means. In this paper, we propose a two-step procedure which incorporates label information into the clustering process by efficiently generating a large and informative vocabulary using class-wise k-means and reducing its size by agglomerative information bottleneck (AIB). We introduce an extension of the AIB procedure for multi-label problems and show that this two-step approach improves the classification results while reducing computation time compared to the vanilla k-means. We analyse the reasons for the performance gain on the PASCAL VOC 2007 data set.

• Stationary Common Spatial Patterns for Non-Stationary EEG Data

  Authors: Wojciech Wojcikiewicz, Carmen Vidaurre, Motoaki Kawanabe

  01/2010

• Enhancing Image Classification with Class-Wise Clustered Vocabularies

  Authors: Wojciech Wojcikiewicz, Alexander Binder, Motoaki Kawanabe

  Pattern Recognition (ICPR), 2010 20th International Conference on; 01/2010

  In recent years bag-of-visual-words representations have gained increasing popularity in the field of image classification. Their performance highly relies on creating a good visual vocabulary from aIn recent years bag-of-visual-words representations have gained increasing popularity in the field of image classification. Their performance highly relies on creating a good visual vocabulary from a set of image features (e.g. SIFT). For real-world photo archives such as Flicker, codebooks with larger than a few thousand words are desirable, which is infeasible by the standard k-means clustering. In this paper, we propose a two-step procedure which can generate more informative codebooks efficiently by class-wise k-means and a novel procedure for word selection. Our approach was compared favorably to the standard k-means procedure on the PASCAL VOC data sets.