Novel approach for multidimensional data reconstruction and compression

In this paper we present a powerful approach for noisy data reconstruction and also for data compression based on our algorithms for tensor factorization and decomposition [10], [9]. This approach has many potential applications in computational neuroscience, multi-sensory, multidimensional data analysis and text mining. Our algorithms are locally stable and work well for sufficiently sparse data even if in the heavy noisy case. Moreover, the proposed approach provides promise applications for real world data. The extensive experimental results confirm the validity and high performance of the developed algorithms not only for synthetic benchmarks but also for real-world data, especially, with usage of the multi-layer hierarchical approach [4], [9].