Kernel Nearest-Neighbor Algorithm
Neural Processing Letters
Evaluation of distance metrics for recognition based on non-negative matrix factorization
Pattern Recognition Letters
Discriminant Basis for Object Classification
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Local Non-Negative Matrix Factorization as a Visual Representation
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Introducing a weighted non-negative matrix factorization for image classification
Pattern Recognition Letters
Matrix Dimensionality Reduction for Mining Web Logs
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Efficient BRDF importance sampling using a factored representation
ACM SIGGRAPH 2004 Papers
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Modeling distances in large-scale networks by matrix factorization
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Some new features for protein fold prediction
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A "nonnegative PCA" algorithm for independent component analysis
IEEE Transactions on Neural Networks
Fast communication: Unsupervised data reduction
Signal Processing
Nonnegative matrix factorization with quadratic programming
Neurocomputing
Blind Image Separation Using Nonnegative Matrix Factorization with Gibbs Smoothing
Neural Information Processing
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
An evolutionary approach for achieving scalability with general regression neural networks
Natural Computing: an international journal
Non-monotone projection gradient method for non-negative matrix factorization
Computational Optimization and Applications
Solving non-negative matrix factorization by alternating least squares with a modified strategy
Data Mining and Knowledge Discovery
Modified subspace Barzilai-Borwein gradient method for non-negative matrix factorization
Computational Optimization and Applications
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Linear and unsupervised dimensionality reduction via matrix factorization with nonnegativity constraints is studied. Because of these constraints, it stands apart from other linear dimensionality reduction methods. Here we explore nonnegative matrix factorization in combination with three nearest-neighbor classifiers for protein fold recognition. Since typically matrix factorization is iteratively done, convergence, can be slow. To speed up convergence, we perform feature scaling (normalization) prior to the beginning of iterations. This results in a significantly (more than 11 times) faster algorithm. Justification of why it happens is provided. Another modification of the standard nonnegative matrix factorization algorithm is concerned with combining two known techniques for mapping unseen data. This operation is typically necessary before classifying the data in low-dimensional space. Combining two mapping techniques can yield better accuracy than using either technique alone. The gains, however, depend on the state of the random number generator used for initialization of iterations, a classifier, and its parameters. In particular, when employing the best out of three classifiers and reducing the original dimensionality by around 30%, these gains can reach more than 4%, compared to the classification in the original, high-dimensional space.