Principle of Learning Metrics for Exploratory Data Analysis
Journal of VLSI Signal Processing Systems
Locally linear metric adaptation for semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Improved learning of Riemannian metrics for exploratory analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Supervised Isomap with Explicit Mapping
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Parametric Embedding for Class Visualization
Neural Computation
Comparison of visualization methods for an atlas of gene expression data sets
Information Visualization
Supervised semi-definite embedding for email data cleaning and visualization
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
Discriminative components of data
IEEE Transactions on Neural Networks
Graph visualization with latent variable models
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Relational generative topographic mapping
Neurocomputing
Relevance learning in generative topographic mapping
Neurocomputing
Data organization and visualization using self-sorting map
Proceedings of Graphics Interface 2011
Visualizing multidimensional data through multilayer perceptron maps
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
A general framework for dimensionality reduction for large data sets
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
A general framework for dimensionality-reducing data visualization mapping
Neural Computation
Incorporating visualisation quality measures to curvilinear component analysis
Information Sciences: an International Journal
How to quantitatively compare data dissimilarities for unsupervised machine learning?
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Discriminative dimensionality reduction mappings
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Visualizing the quality of dimensionality reduction
Neurocomputing
Directing exploratory search with interactive intent modeling
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Using nonlinear dimensionality reduction to visualize classifiers
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Artificial Intelligence in Medicine
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Nonlinear dimensionality reduction methods are often used to visualize high-dimensional data, although the existing methods have been designed for other related tasks such as manifold learning. It has been difficult to assess the quality of visualizations since the task has not been well-defined. We give a rigorous definition for a specific visualization task, resulting in quantifiable goodness measures and new visualization methods. The task is information retrieval given the visualization: to find similar data based on the similarities shown on the display. The fundamental tradeoff between precision and recall of information retrieval can then be quantified in visualizations as well. The user needs to give the relative cost of missing similar points vs. retrieving dissimilar points, after which the total cost can be measured. We then introduce a new method NeRV (neighbor retrieval visualizer) which produces an optimal visualization by minimizing the cost. We further derive a variant for supervised visualization; class information is taken rigorously into account when computing the similarity relationships. We show empirically that the unsupervised version outperforms existing unsupervised dimensionality reduction methods in the visualization task, and the supervised version outperforms existing supervised methods.