Controlling Sparseness in Non-negative Tensor Factorization

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.

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Matthias Heiler, Christoph Schnörr
• Keywords: Classical Ntf Codes, Computer Vision, ECCV 2006, Non-Negative Matrix Factorization, Non-negative Tensor Factorization, Sequential Conic Programming