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- Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe, K. Muller
- IEEE Signal Processing Magazine
- 2008

Due to the volume conduction multichannel electroencephalogram (EEG) recordings give a rather blurred image of brain activity. Therefore spatial filters are extremely useful in single-trial analysis in order to improve the signal-to-noise ratio. There are powerful methods from machine learning and signal processing that permit the optimization of… (More)

- Ryota Tomioka, Klaus-Robert Müller
- NeuroImage
- 2010

We propose a framework for signal analysis of electroencephalography (EEG) that unifies tasks such as feature extraction, feature selection, feature combination, and classification, which are often independently tackled conventionally, under a regularized empirical risk minimization problem. The features are automatically learned, selected and combined… (More)

- Ryota Tomioka, Kazuyuki Aihara
- ICML
- 2007

We propose a method for the classification of matrices. We use a linear classifier with a novel regularization scheme based on the spectral <i>l</i><inf>1</inf>-norm of its coefficient matrix. The spectral regularization not only provides a principled way of complexity control but also enables automatic determination of the rank of the coefficient matrix.… (More)

- Ryota Tomioka, Taiji Suzuki
- NIPS
- 2013

We study a new class of structured Schatten norms for tensors that includes two recently proposed norms (“overlapped” and “latent”) for convex-optimization-based tensor decomposition. Based on the properties of the structured Schatten norms, we analyze the performance of “latent” approach for tensor decomposition, which was empirically found to perform… (More)

Brain-Computer Interfaces can suffer from a large variance of the subject conditions within and across sessions. For example vigilance fluctuations in the individual, variable task involvement, workload etc. alter the characteristics of EEG signals and thus challenge a stable BCI operation. In the present work we aim to define features based on a variant of… (More)

- Sebastian Nowozin, Botond Cseke, Ryota Tomioka
- NIPS
- 2016

Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights. These models are expressive and allow efficient computation of samples and derivatives, but cannot be used for computing… (More)

- Atsuhiro Narita, Kohei Hayashi, Ryota Tomioka, Hisashi Kashima
- Data Mining and Knowledge Discovery
- 2011

Most of the existing analysis methods for tensors (or multi-way arrays) only assume that tensors to be completed are of low rank. However, for example, when they are applied to tensor completion problems, their prediction accuracy tends to be significantly worse when only a limited number of entries are observed. In this paper, we propose to use… (More)

- Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima
- NIPS
- 2011

We analyze the statistical performance of a recently proposed convex tensor decomposition algorithm. Conventionally tensor decomposition has been formulated as non-convex optimization problems, which hindered the analysis of their performance. We show under some conditions that the mean squared error of the convex method scales linearly with the quantity we… (More)

- Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka
- NIPS
- 2014

We study a multitask learning problem in which each task is parametrized by a weight vector and indexed by a pair of indices, which can be e.g, (consumer, time). The weight vectors can be collected into a tensor and the (multilinear-)rank of the tensor controls the amount of sharing of information among tasks. Two types of convex relaxations have recently… (More)

A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the possible nonstationarity in the data from the datablock the method is trained on and that the method is applied to. Assuming the joint distributions of the whitened signal and the class label to be identical in two blocks, where the whitening is… (More)