Search Results for author: Akira Imakura

Found 18 papers, 3 papers with code

New Solutions Based on the Generalized Eigenvalue Problem for the Data Collaboration Analysis

no code implementations22 Apr 2024 Yuta Kawakami, Yuichi Takano, Akira Imakura

In recent years, the accumulation of data across various institutions has garnered attention for the technology of confidential data analysis, which improves analytical accuracy by sharing data between multiple institutions while protecting sensitive information.

Estimation of conditional average treatment effects on distributed data: A privacy-preserving approach

no code implementations5 Feb 2024 Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai

Second, our method enables collaborative estimation between different parties as well as multiple time points because the dimensionality-reduced intermediate representations can be accumulated.

Privacy Preserving

Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings

no code implementations1 Aug 2023 Akihiro Mizoguchi, Anna Bogdanova, Akira Imakura, Tetsuya Sakurai

However, federated learning is encumbered by low accuracy in not identically and independently distributed (non-IID) settings, i. e., data partitioning has a large label bias, and is considered unsuitable for compound datasets, which tend to have large label bias.

Federated Learning

Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration

no code implementations6 Dec 2022 Anna Bogdanova, Akira Imakura, Tetsuya Sakurai, Tomoya Fujii, Teppei Sakamoto, Hiroyuki Abe

Transparency of Machine Learning models used for decision support in various industries becomes essential for ensuring their ethical use.

Privacy Preserving

Non-readily identifiable data collaboration analysis for multiple datasets including personal information

no code implementations31 Aug 2022 Akira Imakura, Tetsuya Sakurai, Yukihiko Okada, Tomoya Fujii, Teppei Sakamoto, Hiroyuki Abe

This study then proposes a non-readily identifiable DC analysis only sharing non-readily identifiable data for multiple medical datasets including personal information.

Another Use of SMOTE for Interpretable Data Collaboration Analysis

no code implementations26 Aug 2022 Akira Imakura, Masateru Kihira, Yukihiko Okada, Tetsuya Sakurai

DC analysis centralizes individually constructed dimensionality-reduced intermediate representations and realizes integrated analysis via collaboration representations without sharing the original data.

feature selection Privacy Preserving

Collaborative causal inference on distributed data

no code implementations16 Aug 2022 Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai

Many existing methods for distributed data focus on resolving the lack of subjects (samples) and can only reduce random errors in estimating treatment effects.

Causal Inference Dimensionality Reduction

Fast Algorithm for Low-Rank Tensor Completion in Delay-Embedded Space

no code implementations CVPR 2022 Ryuki Yamamoto, Hidekata Hontani, Akira Imakura, Tatsuya Yokota

Tensor completion using multiway delay-embedding transform (MDT) (or Hankelization) suffers from the large memory requirement and high computational cost in spite of its high potentiality for the image modeling.

LSEC: Large-scale spectral ensemble clustering

1 code implementation18 Jun 2021 Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai

In LSEC, a large-scale spectral clustering based efficient ensemble generation framework is designed to generate various base clusterings within a low computational complexity.


Divide-and-conquer based Large-Scale Spectral Clustering

1 code implementation30 Apr 2021 Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai

In this paper, we propose a divide-and-conquer based large-scale spectral clustering method to strike a good balance between efficiency and effectiveness.

Clustering Image/Document Clustering

Accuracy and Privacy Evaluations of Collaborative Data Analysis

no code implementations27 Jan 2021 Akira Imakura, Anna Bogdanova, Takaya Yamazoe, Kazumasa Omote, Tetsuya Sakurai

Distributed data analysis without revealing the individual data has recently attracted significant attention in several applications.

Dimensionality Reduction Federated Learning

Ensemble Learning for Spectral Clustering

1 code implementation20 Nov 2020 Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai

Instead of directly using the clustering results obtained from each base spectral clustering algorithm, the proposed method learns a robust presentation of graph Laplacian by ensemble learning from the spectral embedding of each base spectral clustering algorithm.

Clustering Ensemble Learning +1

Interpretable collaborative data analysis on distributed data

no code implementations9 Nov 2020 Akira Imakura, Hiroaki Inaba, Yukihiko Okada, Tetsuya Sakurai

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 distributed data.

Federated Learning

Data collaboration analysis for distributed datasets

no code implementations20 Feb 2019 Akira Imakura, Tetsuya Sakurai

In this paper, we propose a data collaboration analysis method for distributed datasets.

Privacy Preserving

Alternating optimization method based on nonnegative matrix factorizations for deep neural networks

no code implementations16 May 2016 Tetsuya Sakurai, Akira Imakura, Yuto Inoue, Yasunori Futamura

In this paper, we propose a novel approach for computing weight matrices of fully-connected DNNs by using two types of semi-nonnegative matrix factorizations (semi-NMFs).

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