An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Concept decompositions for large sparse text data using clustering
Machine Learning
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
A tutorial on spectral clustering
Statistics and Computing
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Non-negative Matrix Factorization on Manifold
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Semi-Supervised Learning
Cholesky decomposition rectification for non-negative matrix factorization
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
PCA document reconstruction for email classification
Computational Statistics & Data Analysis
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The curse of dimensionality in data mining and time series prediction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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This paper proposes a novel method to the problem of non-orthogonality of features obtained using the Non-negative Matrix Factorization NMF method. For any given non-negative data matrix, the NMF method provides a learned local representation by approximating the data matrix as a product of two non-negative matrices. However, the non-orthogonality of the features hinders the effective use of the learned representation from the NMF. To overcome this problem, we propose the following steps: calculate the metric in the feature space adapted to the features, apply the Cholesky decomposition to the metric and identify the upper triangular matrix, and use the upper triangular matrix as a linear mapping for the learned representation from the NMF. The proposed method is applied to current NMF-based clustering algorithms and evaluated over real-world datasets. The results indicate that the proposed method improves the performance of those algorithms, and that the method is very robust, even in the presence of a large number of features.