Search Results for author:
Found 8 papers, 3 papers with code
Scalable manifold learning by uniform landmark sampling and constrained locally linear embedding
As a pivotal approach in machine learning and data science, manifold learning aims to uncover the intrinsic low-dimensional structure within complex nonlinear manifolds in high-dimensional space.
Interpreting the Curse of Dimensionality from Distance Concentration and Manifold Effect
By interpreting the causes of the curse of dimensionality, we can better understand the limitations of current models and algorithms, and drive to improve the performance of data analysis and machine learning tasks in high-dimensional space.
MeanCut: A Greedy-Optimized Graph Clustering via Path-based Similarity and Degree Descent Criterion
As the most typical graph clustering method, spectral clustering is popular and attractive due to the remarkable performance, easy implementation, and strong adaptability.
A Robust and Efficient Boundary Point Detection Method by Measuring Local Direction Dispersion
Boundary points pose a significant challenge for machine learning tasks, including classification, clustering, and dimensionality reduction.
Adapting to climate change: Long-term impact of wind resource changes on China's power system resilience
Modern society's reliance on power systems is at risk from the escalating effects of wind-related climate change.
Dynamic Visual Semantic Sub-Embeddings and Fast Re-Ranking
In this work, we propose a Dynamic Visual Semantic Sub-Embeddings framework (DVSE) to reduce the information entropy.
Forecast Network-Wide Traffic States for Multiple Steps Ahead: A Deep Learning Approach Considering Dynamic Non-Local Spatial Correlation and Non-Stationary Temporal Dependency
This model builds on sequence to sequence (seq2seq) architecture to capture temporal feature and relies on graph convolution for aggregating spatial information.
A Hybrid Traffic Speed Forecasting Approach Integrating Wavelet Transform and Motif-based Graph Convolutional Recurrent Neural Network
In this paper, we propose a hybrid approach that learns the spatio-temporal dependency in traffic flows and predicts short-term traffic speeds on a road network.
