ncku-talk2024-08-05

Analysis Seminar

DATE 2024-08-05　11:10-12:30

PLACE 數學系3樓會議室

SPEAKER 吳浩榳教授（Department of Mathematics Courant Institute of Mathematical Sciences, New York University）

TITLE Nonstationary Time Series Analysis with Manifold Learning Techniques

ABSTRACT Spectral embedding is a popular dimension reduction technique, yet scaling it to handle large datasets and ensuring robustness remain challenges. In response, we introduce Robust and Scalable Embedding via Landmark Diffusion (ROSELAND). This method leverages a small set of landmarks to efficiently compute spectral embeddings, generalizing the diffusion map (DM) algorithm while preserving its key properties. We demonstrate ROSELAND's scalability and robustness, provide theoretical insights into its asymptotic behavior, and validate its performance through simulations.

Analysis Seminar

DATE	2024-08-05　11:10-12:30

PLACE	數學系3樓會議室

SPEAKER	吳浩榳教授（Department of Mathematics Courant Institute of Mathematical Sciences, New York University）

TITLE	Nonstationary Time Series Analysis with Manifold Learning Techniques

ABSTRACT	Spectral embedding is a popular dimension reduction technique, yet scaling it to handle large datasets and ensuring robustness remain challenges. In response, we introduce Robust and Scalable Embedding via Landmark Diffusion (ROSELAND). This method leverages a small set of landmarks to efficiently compute spectral embeddings, generalizing the diffusion map (DM) algorithm while preserving its key properties. We demonstrate ROSELAND's scalability and robustness, provide theoretical insights into its asymptotic behavior, and validate its performance through simulations.