GTM: the generative topographic mapping
Neural Computation
Kernel principal component analysis
Advances in kernel methods
Self-Organizing Maps
Nonlinear Projection with the Isotop Method
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
An introduction to nonlinear dimensionality reduction by maximum variance unfolding
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
The Journal of Machine Learning Research
Scale-independent quality criteria for dimensionality reduction
Pattern Recognition Letters
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
The Journal of Machine Learning Research
Extending Sammon mapping with Bregman divergences
Information Sciences: an International Journal
A general framework for dimensionality-reducing data visualization mapping
Neural Computation
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The growing number of dimensionality reduction methods available for data visualization has recently inspired the development of formal measures to evaluate the resulting low-dimensional representation independently from the methods' inherent criteria. Many evaluation measures can be summarized based on the co-ranking matrix. In this work, we analyze the characteristics of the co-ranking framework, focusing on interpretability and controllability in evaluation scenarios where a fine-grained assessment of a given visualization is desired. We extend the framework in two ways: (i) we propose how to link the evaluation to point-wise quality measures which can be used directly to augment the evaluated visualization and highlight erroneous regions; (ii) we improve the parameterization of the quality measure to offer more direct control over the evaluation's focus, and thus help the user to investigate more specific characteristics of the visualization.