2024 Fiscal Year Research-status Report
Development of learning subspace-based methods for pattern recognition
| Project/Area Number |
22K17960
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| Research Institution | National Institute of Advanced Industrial Science and Technology |
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Principal Investigator |
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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| Project Period (FY) |
2022-04-01 – 2026-03-31
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| Keywords | subspace learning / deep neural networks |
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| Outline of Annual Research Achievements |
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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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
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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| Strategy for Future Research Activity |
We conclude the research project by finishing all the experiments and submitting the remaining work.
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