Nonlinear Principal Manifolds --- Adaptive Hybrid Learning Approaches
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
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Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Similarity-Based Clustering
Adaptive nonlinear manifolds and their applications to pattern recognition
Information Sciences: an International Journal
Time-sensitive feature mining for temporal sequence classification
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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Optical Memory and Neural Networks
Relevance learning in generative topographic mapping
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KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Neural Processing Letters
Intelligent acoustic rotor speed estimation for an autonomous helicopter
Applied Soft Computing
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IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Analysis of space-time flow structures by optimization and visualization methods
Transactions on Computational Science XIX
A generative model and a generalized trust region Newton method for noise reduction
Computational Optimization and Applications
Visualizing motional correlations in molecular dynamics using geometric deformations
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial "PCA and K-meansdecipher genome". The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD students and researchers in computer sciences, applied mathematics and statistics.