Convex programs with an additional reverse convex constraint
Journal of Optimization Theory and Applications
Pattern Recognition Letters
The Journal of Machine Learning Research
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Damped Newton Algorithms for Matrix Factorization with Missing Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Sparse Image Coding Using a 3D Non-Negative Tensor Factorization
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Non-Negative Sparse Image Codes by Convex Programming
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Non-negative tensor factorization with applications to statistics and computer vision
ICML '05 Proceedings of the 22nd international conference on Machine learning
Algorithms for sparse nonnegative tucker decompositions
Neural Computation
Supervised dimensionality reduction via sequential semidefinite programming
Pattern Recognition
Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Regularized Alternating Least Squares Algorithms for Non-negative Matrix/Tensor Factorization
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Sparse Super Symmetric Tensor Factorization
Neural Information Processing
Flexible Component Analysis for Sparse, Smooth, Nonnegative Coding or Representation
Neural Information Processing
Multiresolution approach in computing NTF
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Non-negative tensor factorization applied to music genre classification
IEEE Transactions on Audio, Speech, and Language Processing
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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.