Attention-Guided Deep Adversarial Temporal Subspace Clustering (A-DATSC) Model for multivariate spatiotemporal data
Fuente:
arXiv
Saved in:
| Main Authors: | Nji, Francis Ndikum, Janeja, Vandana, Wang, Jianwu |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data
by: Nji, Francis Ndikum, et al.
Published: (2024)
by: Nji, Francis Ndikum, et al.
Published: (2024)
B-TGAT: A Bi-directional Temporal Graph Attention Transformer for Clustering Multivariate Spatiotemporal Data
by: Nji, Francis Ndikum, et al.
Published: (2025)
by: Nji, Francis Ndikum, et al.
Published: (2025)
FAConvLSTM: Factorized-Attention ConvLSTM for Efficient Feature Extraction in Multivariate Climate Data
by: Nji, Francis Ndikum, et al.
Published: (2026)
by: Nji, Francis Ndikum, et al.
Published: (2026)
Knowledge-Guided Time-Varying Causal Inference for Arctic Sea Ice Dynamics
by: Sampath, Akila, et al.
Published: (2026)
by: Sampath, Akila, et al.
Published: (2026)
Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction
by: Sampath, Akila, et al.
Published: (2026)
by: Sampath, Akila, et al.
Published: (2026)
Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization
by: Ndikum, Philip, et al.
Published: (2024)
by: Ndikum, Philip, et al.
Published: (2024)
Harnessing Feature Clustering For Enhanced Anomaly Detection With Variational Autoencoder And Dynamic Threshold
by: Ale, Tolulope, et al.
Published: (2024)
by: Ale, Tolulope, et al.
Published: (2024)
IDRIFTNET: Physics-Driven Spatiotemporal Deep Learning for Iceberg Drift Forecasting
by: Putatunda, Rohan, et al.
Published: (2025)
by: Putatunda, Rohan, et al.
Published: (2025)
Advancing climate model interpretability: Feature attribution for Arctic melt anomalies
by: Ale, Tolulope, et al.
Published: (2025)
by: Ale, Tolulope, et al.
Published: (2025)
DeepTopoNet: A Framework for Subglacial Topography Estimation on the Greenland Ice Sheets
by: Tama, Bayu Adhi, et al.
Published: (2025)
by: Tama, Bayu Adhi, et al.
Published: (2025)
Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residuals
by: Tama, Bayu Adhi, et al.
Published: (2025)
by: Tama, Bayu Adhi, et al.
Published: (2025)
Multi-view Deep Subspace Clustering Networks
by: Zhu, Pengfei, et al.
Published: (2019)
by: Zhu, Pengfei, et al.
Published: (2019)
Subspace Clustering of Subspaces: Unifying Canonical Correlation Analysis and Subspace Clustering
by: Karakasis, Paris A., et al.
Published: (2025)
by: Karakasis, Paris A., et al.
Published: (2025)
Federated Deep Subspace Clustering
by: Zhang, Yupei, et al.
Published: (2024)
by: Zhang, Yupei, et al.
Published: (2024)
Causal Feedback Discovery using Convergence Cross Mapping on Sea Ice Data
by: Nji, Francis, et al.
Published: (2025)
by: Nji, Francis, et al.
Published: (2025)
Deep Temporal Graph Clustering
by: Liu, Meng, et al.
Published: (2023)
by: Liu, Meng, et al.
Published: (2023)
Enhanced Latent Multi-view Subspace Clustering
by: Shi, Long, et al.
Published: (2023)
by: Shi, Long, et al.
Published: (2023)
Exploring a Principled Framework for Deep Subspace Clustering
by: Meng, Xianghan, et al.
Published: (2025)
by: Meng, Xianghan, et al.
Published: (2025)
Cluster weighted models with multivariate skewed distributions for functional data
by: Anton, Cristina, et al.
Published: (2025)
by: Anton, Cristina, et al.
Published: (2025)
Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasets
by: Liu, Meng, et al.
Published: (2026)
by: Liu, Meng, et al.
Published: (2026)
Attention-Only Transformers via Unrolled Subspace Denoising
by: Wang, Peng, et al.
Published: (2025)
by: Wang, Peng, et al.
Published: (2025)
Model Unlearning via Sparse Autoencoder Subspace Guided Projections
by: Wang, Xu, et al.
Published: (2025)
by: Wang, Xu, et al.
Published: (2025)
Scalable Deep Subspace Clustering Network
by: Mrabah, Nairouz, et al.
Published: (2025)
by: Mrabah, Nairouz, et al.
Published: (2025)
Masked Subspace Clustering Methods
by: Song, Jiebo, et al.
Published: (2025)
by: Song, Jiebo, et al.
Published: (2025)
TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
by: Faruque, Omar, et al.
Published: (2024)
by: Faruque, Omar, et al.
Published: (2024)
Temporal Subspace Clustering for Molecular Dynamics Data
by: Beer, Anna, et al.
Published: (2024)
by: Beer, Anna, et al.
Published: (2024)
Adversarial Spatio-Temporal Attention Networks for Epileptic Seizure Forecasting
by: Li, Zan, et al.
Published: (2025)
by: Li, Zan, et al.
Published: (2025)
ALPCAHUS: Subspace Clustering for Heteroscedastic Data
by: Cavazos, Javier Salazar, et al.
Published: (2025)
by: Cavazos, Javier Salazar, et al.
Published: (2025)
TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
by: Faruque, Omar, et al.
Published: (2026)
by: Faruque, Omar, et al.
Published: (2026)
Cluster-Aware Attention-Based Deep Reinforcement Learning for Pickup and Delivery Problems
by: Wang, Wentao, et al.
Published: (2026)
by: Wang, Wentao, et al.
Published: (2026)
Explaining Clustering of Ecological Momentary Assessment Data Through Temporal and Feature Attention
by: Ntekouli, Mandani, et al.
Published: (2024)
by: Ntekouli, Mandani, et al.
Published: (2024)
SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation
by: Shmuel, Dor H., et al.
Published: (2023)
by: Shmuel, Dor H., et al.
Published: (2023)
Spectral Subspace Clustering for Attributed Graphs
by: Lin, Xiaoyang, et al.
Published: (2024)
by: Lin, Xiaoyang, et al.
Published: (2024)
Subspace Defense: Discarding Adversarial Perturbations by Learning a Subspace for Clean Signals
by: Zheng, Rui, et al.
Published: (2024)
by: Zheng, Rui, et al.
Published: (2024)
Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models
by: Rauba, Paulius, et al.
Published: (2025)
by: Rauba, Paulius, et al.
Published: (2025)
SUPRA: Subspace Parameterized Attention for Neural Operator on General Domains
by: Yang, Zherui, et al.
Published: (2025)
by: Yang, Zherui, et al.
Published: (2025)
Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering
by: Wang, Peng, et al.
Published: (2024)
by: Wang, Peng, et al.
Published: (2024)
Deep Learning for Subspace Regression
by: Fanaskov, Vladimir, et al.
Published: (2025)
by: Fanaskov, Vladimir, et al.
Published: (2025)
ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution
by: Zhou, Zhengyang, et al.
Published: (2024)
by: Zhou, Zhengyang, et al.
Published: (2024)
Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization
by: Liu, Ganlin, et al.
Published: (2024)
by: Liu, Ganlin, et al.
Published: (2024)
Similar Items
-
Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data
by: Nji, Francis Ndikum, et al.
Published: (2024) -
B-TGAT: A Bi-directional Temporal Graph Attention Transformer for Clustering Multivariate Spatiotemporal Data
by: Nji, Francis Ndikum, et al.
Published: (2025) -
FAConvLSTM: Factorized-Attention ConvLSTM for Efficient Feature Extraction in Multivariate Climate Data
by: Nji, Francis Ndikum, et al.
Published: (2026) -
Knowledge-Guided Time-Varying Causal Inference for Arctic Sea Ice Dynamics
by: Sampath, Akila, et al.
Published: (2026) -
Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction
by: Sampath, Akila, et al.
Published: (2026)