A fast fixed-point algorithm for independent component analysis
Neural Computation
New approximations of differential entropy for independent component analysis and projection pursuit
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Parallel Coordinates: Visual Multidimensional Geometry and Its Applications
Parallel Coordinates: Visual Multidimensional Geometry and Its Applications
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Automatic Choice of the Number of Nearest Neighbors in Locally Linear Embedding
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
iPCA: an interactive system for PCA-based visual analytics
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
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One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system but which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21.