Self-organizing maps
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Analysis of Parliamentary Election Results and Socio-Economic Situation Using Self-Organizing Map
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
RankVisu: Mapping from the neighborhood network
Neurocomputing
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
The Journal of Machine Learning Research
Visualizing sets of partial rankings
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Visualization of topology representing networks
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Scale-independent quality criteria for dimensionality reduction
Pattern Recognition Letters
Multilevel manifold learning with application to spectral clustering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Visualizing dissimilarity data using generative topographic mapping
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Multidimensional data visualization applied for user's questionnaire data quality assessment
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Visualising the structure of document search results: a comparison of graph theoretic approaches
Information Visualization
Relational generative topographic mapping
Neurocomputing
Relevance learning in generative topographic mapping
Neurocomputing
Visualizing multidimensional data through multilayer perceptron maps
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Instant approximate 1-center on road networks via embeddings
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Distributed spectral cluster management: a method for building dynamic publish/subscribe systems
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Histology image analysis for carcinoma detection and grading
Computer Methods and Programs in Biomedicine
Fast k-clustering queries on embeddings of road networks
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Neurocomputing
Incorporating visualisation quality measures to curvilinear component analysis
Information Sciences: an International Journal
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
Visualizing the quality of dimensionality reduction
Neurocomputing
Stress functions for nonlinear dimension reduction, proximity analysis, and graph drawing
The Journal of Machine Learning Research
Engineering Applications of Artificial Intelligence
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In a visualization task, every nonlinear projection method needs to make a compromise between trustworthiness and continuity. In a trustworthy projection the visualized proximities hold in the original data as well, whereas a continuous projection visualizes all proximities of the original data. We show experimentally that one of the multidimensional scaling methods, curvilinear components analysis, is good at maximizing trustworthiness. We then extend it to focus on local proximities both in the input and output space, and to explicitly make a user-tunable parameterized compromise between trustworthiness and continuity. The new method compares favorably to alternative nonlinear projection methods.