| 研究課題/領域番号 |
22K17960
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分61030:知能情報学関連
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| 研究機関 | 国立研究開発法人産業技術総合研究所 |
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研究代表者 |
SALESDESOUZA LINCON 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 研究員 (40912481)
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| 研究期間 (年度) |
2022-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2024年度)
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| 配分額 *注記 |
4,680千円 (直接経費: 3,600千円、間接経費: 1,080千円)
2025年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2024年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2023年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
2022年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
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| キーワード | subspace learning / deep neural networks / Subspace learning / Deep neural networks / Manifold optimization / Subspace methods / Pattern recognition |
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| 研究開始時の研究の概要 |
We research a new algorithm for pattern recognition, which are computer programs that allow a machine to automatically recognize regularities in data, such as target objects and events. We mainly focus on the case of recognizing patterns in given multiple images of one object, addressing some inabilities of the current technology called deep learning.
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| 研究実績の概要 |
In year 2024, we continued working on the combination of neural networks and subspace learning. We have worked in an application to environmental sound classification, where we propose a method using an ensemble of subspace representations of latent features obtained from various neural network-based models. We were able to successfully achieve a competitive performance on real data, and published this result on the journal Applied Acoustics. We also developed a method for data analysis in a Riemannian geometry. We specifically proposed a time-series data embedding technique that preserves manifold curvature and orientation. We showcased our method in a setting with subspace representation, with an use case of analyzing the temporal information encoded in neural activation dynamics.
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| 現在までの達成度 |
現在までの達成度
2: おおむね順調に進展している
理由
We have been able apply our methods to environmental sound classification, and to develop a manifold data analysis method and apply to analyze neural data.
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| 今後の研究の推進方策 |
We conclude the research project by finishing all the experiments and submitting the remaining work.
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