10 citations · 25 across the 9 of their papers we have counts for
6 papers · 1 filter
Accuracy and Privacy Evaluations of Collaborative Data Analysis
Akira Imakura, Anna Bogdanova, Takaya Yamazoe +2
Distributed data analysis without revealing the individual data has recently attracted significant attention in several applications. A collaborative data analysis through sharing…
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
Anna Bogdanova, Akie Nakai, Yukihiko Okada +2
Dimensionality Reduction is a commonly used element in a machine learning pipeline that helps to extract important features from high-dimensional data. In this work, we explore an…
Interpretable collaborative data analysis on distributed data
Akira Imakura, Hiroaki Inaba, Yukihiko Okada +1
This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems, which is an emerging technology to analyze distr…
Multiclass spectral feature scaling method for dimensionality reduction
Momo Matsuda, Keiichi Morikuni, Akira Imakura +2
Irregular features disrupt the desired classification. In this paper, we consider aggressively modifying scales of features in the original space according to the label information…
Data collaboration analysis for distributed datasets
Akira Imakura, Tetsuya Sakurai
In this paper, we propose a data collaboration analysis method for distributed datasets. The proposed method is a centralized machine learning while training datasets and models re…
Alternating optimization method based on nonnegative matrix factorizations for deep neural networks
Tetsuya Sakurai, Akira Imakura, Yuto Inoue +1
The backpropagation algorithm for calculating gradients has been widely used in computation of weights for deep neural networks (DNNs). This method requires derivatives of objectiv…