Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Medical Image Registration Using Geometric Hashing
IEEE Computational Science & Engineering
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Alignment by maximization of mutual information
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
An introduction to variable and feature selection
The Journal of Machine Learning Research
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Data Fusion and Multicue Data Matching by Diffusion Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Regularized Sensor Fusion: Application to Localization With Distributed Motion Sensors
Journal of VLSI Signal Processing Systems
Optimal dimensionality reduction of sensor data in multisensor estimation fusion
IEEE Transactions on Signal Processing
Cross-Modal Localization via Sparsity
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Audiovisual Synchronization and Fusion Using Canonical Correlation Analysis
IEEE Transactions on Multimedia
Graph Laplacian Tomography From Unknown Random Projections
IEEE Transactions on Image Processing
A contour-based approach to multisensor image registration
IEEE Transactions on Image Processing
Joint manifolds for data fusion
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
| Hi-index | 35.68 |
Data fusion is a natural and common approach to recovering the state of physical systems. But the dissimilar appearance of different sensors remains a fundamental obstacle.We propose a unified embedding scheme for multisensory data,based on the spectral diffusion framework, which addresses this issue. Our scheme is purely data-driven and assumes no a priori statistical or deterministic models of the data sources. To extract the underlying structure, we first embed separately each input channel; the resultant structures are then combined in diffusion coordinates. In particular, as different sensors sample similar phenomena with different sampling densities, we apply the density invariant Laplace-Beltrami embedding. This is a fundamental issue in multisensor acquisition and processing, overlooked in prior approaches. We extend previous work on group recognition and suggest a novel approach to the selection of diffusion coordinates.To verify our approach, we demonstrate performance improvements in audio/visual speech recognition.