A nonmonotone line search technique for Newton's method
SIAM Journal on Numerical Analysis
An assessment of nonmonotone linesearch techniques for unconstrained optimization
SIAM Journal on Scientific Computing
Modified Two-Point Stepsize Gradient Methods for Unconstrained Optimization
Computational Optimization and Applications
Nonmonotone Spectral Projected Gradient Methods on Convex Sets
SIAM Journal on Optimization
Introducing a weighted non-negative matrix factorization for image classification
Pattern Recognition Letters
A Nonmonotone Line Search Technique and Its Application to Unconstrained Optimization
SIAM Journal on Optimization
Non-negative matrix factorization based methods for object recognition
Pattern Recognition Letters
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Projected Barzilai-Borwein methods for large-scale box-constrained quadratic programming
Numerische Mathematik
On the asymptotic behaviour of some new gradient methods
Mathematical Programming: Series A and B
Nonnegative features of spectro-temporal sounds for classification
Pattern Recognition Letters
Nonsmooth Nonnegative Matrix Factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Image Components for Object Recognition
The Journal of Machine Learning Research
Fast nonnegative matrix factorization and its application for protein fold recognition
EURASIP Journal on Applied Signal Processing
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Non-negative matrix factorization with quasi-newton optimization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
On the convergence of the block nonlinear Gauss-Seidel method under convex constraints
Operations Research Letters
On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization
IEEE Transactions on Neural Networks
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Non-negative matrix factorization (NMF) is a problem to obtain a representation of data using non-negativity constraints. Since the NMF was first proposed by Lee, NMF has attracted much attention for over a decade and has been successfully applied to numerous data analysis problems. Recent years, many variants of NMF have been proposed. Common methods are: iterative multiplicative update algorithms, gradient descent methods, alternating least squares (ANLS). Since alternating least squares has nice optimization properties, various optimization methods can be used to solve ANLS's subproblems. In this paper, we propose a modified subspace Barzilai-Borwein for subproblems of ANLS. Moreover, we propose a modified strategy for ANLS. Global convergence results of our algorithm are established. The results of numerical experiments are reported to show the effectiveness of the proposed algorithm.