The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Finger surface as a biometric identifier
Computer Vision and Image Understanding
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce
Proceedings of the 19th international conference on World wide web
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
Linear and nonlinear projective nonnegative matrix factorization
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks - Part 1
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Projective Nonnegative Matrix Factorization (PNMF) is one of the recent methods for computing low-rank approximations to data matrices. It is advantageous in many practical application domains such as clustering, graph partitioning, and sparse feature extraction. However, up to now a scalable implementation of PNMF for large-scale machine learning problems has been lacking. Here we provide an online algorithm for fast PNMF learning with low memory cost. The new algorithm simply applies multiplicative update rules iteratively on small subsets of the data, with historical data naturally accumulated. Consequently users do not need extra efforts to tune any optimization parameters such as learning rates or the history weight. In addition to scalability and convenience, empirical studies on synthetic and real-world datasets indicate that our online algorithm runs much faster than the existing batch version.