Proceedings of the 1998 conference on Advances in neural information processing systems II
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Projective nonnegative matrix factorization for image compression and feature extraction
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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Projective Nonnegative Matrix Factorization (PNMF) has demonstrated advantages in both sparse feature extraction and clustering. However, PNMF requires users to specify the column rank of the approximative projection matrix, the value of which is unknown before-hand. In this paper, we propose a method called ARDPNMF to automatically determine the column rank in PNMF. Our method is based on automatic relevance determination (ARD) with Jeffrey's prior. After deriving the multiplicative update rule using the expectation-maximization technique for ARDPNMF, we test it on various synthetic and real-world datasets for feature extraction and clustering applications to show the effectiveness of our algorithm. For FERET faces and the Swimmer dataset, interpretable number of features are obtained correctly via our algorithm. Several UCI datasets for clustering are also tested, in which we find that ARDPNMF can estimate the number of clusters quite accurately with low deviation and good cluster purity.