Abstract Non-negative tensor factorization (NTF) has recently been proposed as sparse and efficient image representation (Welling and Weber, Patt. Rec. Let., 2001). Until now, sparsity of the tensor factorization has been empirically observed in many cases, but there was no systematic way to control it. In this work, we show that a sparsity measure recently proposed for non-negative matrix factorization (Hoyer, J. Mach. Learn. Res., 2004) applies to NTF and allows precise control over sparseness of the resulting factorization. We devise an algorithm based on sequential conic programming and show improved performance over classical NTF codes on artificial and on real-world data sets.
Controlling Sparseness in Non-negative Tensor Factorization
2006-01-01
12 pages
Article/Chapter (Book)
Electronic Resource
English
Controlling Sparseness in Non-negative Tensor Factorization
British Library Conference Proceedings | 2006
|Sparse Image Coding Using a 3D Non-Negative Tensor Factorization
British Library Conference Proceedings | 2005
|Multi-way Clustering Using Super-Symmetric Non-negative Tensor Factorization
Springer Verlag | 2006
|Multi-way Clustering Using Super-Symmetric Non-negative Tensor Factorization
British Library Conference Proceedings | 2006
|