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Found 6 papers, 1 papers with code
Beyond Low Rank: A Graph-Based Propagation Approach to Tensor Completion for Multi-Acquisition Scenarios
Referred to as GraphProp, the method propagates observed entries around a graph-based representation of the tensor in order to recover the missing entries.
Improving embedding of graphs with missing data by soft manifolds
The reliability of graph embeddings directly depends on how much the geometry of the continuous space matches the graph structure.
A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection
This system provides several constraints and assumptions on the data properties that might be not valid for multimodal data analysis, especially when large scale datasets collected from heterogeneous sources are considered, so that the accuracy and robustness of the outcomes might be severely jeopardized.
Improving Global Forest Mapping by Semi-automatic Sample Labeling with Deep Learning on Google Earth Images
To provide guidelines for users and producers, it is urgent to produce a validation sample set at the global level.
Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction
Nonetheless, when applied to multimodal datasets (i. e., datasets acquired by means of multiple sensing techniques or strategies), the state-of-theart methods for ensemble learning and transfer learning might show some limitations.
Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions
The experimental results demonstrate the superiority of the proposed method, which not only outperforms all baselines -with the highest F1 scores of 87. 40% on the building change detection dataset and 92. 94% on the change detection dataset -but also obtains the best accuracies on experiments performed with images having a 4x and 8x resolution difference.
