Download now Free registration required
Many real world data sets exhibit an embedding of low-dimensional structure in a high-dimensional manifold. Examples include images, videos and internet traffic data. It is of great significance to reduce the storage requirements and computational complexity when the data dimension is high. Therefore, the authors consider the problem of reconstructing a data stream from a small subset of its entries, where the data is assumed to lie in a low-dimensional linear subspace, possibly corrupted by noise. They further consider tracking the change of the underlying subspace, which can be applied to applications such as video denoising, network monitoring and anomaly detection.
- Format: PDF
- Size: 4679.68 KB