Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Visual Analytics: Scope and Challenges
Visual Data Mining
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
Adaptive relevance matrices in learning vector quantization
Neural Computation
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
The Journal of Machine Learning Research
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With electronic data increasing dramatically in almost all areas of research, a plethora of new techniques for automatic dimensionality reduction and data visualization has become available in recent years. These offer an interface which allows humans to rapidly scan through large volumes of data. With data sets becoming larger and larger, however, the standard methods can no longer be applied directly. Random subsampling or prior clustering still being one of the most popular solutions in this case, we discuss a principled alternative and formalize the approaches under a general perspectives of dimensionality reduction as cost optimization. We have a first look at the question whether these techniques can be accompanied by theoretical guarantees.