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Found 7 papers, 3 papers with code
How can classical multidimensional scaling go wrong?
While $D_l$ is not metric, when given as input to cMDS instead of $D$, it empirically results in solutions whose distance to $D$ does not increase when we increase the dimension and the classification accuracy degrades less than the cMDS solution.
An Analysis of COVID-19 Knowledge Graph Construction and Applications
The construction and application of knowledge graphs have seen a rapid increase across many disciplines in recent years.
Training Data Size Induced Double Descent For Denoising Neural Networks and the Role of Training Noise Level
In fact the generalization error versus number of of training data points is a double descent curve.
Tree! I am no Tree! I am a Low Dimensional Hyperbolic Embedding
Given data, finding a faithful low-dimensional hyperbolic embedding of the data is a key method by which we can extract hierarchical information or learn representative geometric features of the data.
Project and Forget: Solving Large-Scale Metric Constrained Problems
Given a set of dissimilarity measurements amongst data points, determining what metric representation is most "consistent" with the input measurements or the metric that best captures the relevant geometric features of the data is a key step in many machine learning algorithms.
Project and Forget: Solving Large Scale Metric Constrained Problems
Given a set of distances amongst points, determining what metric representation is most “consistent” with the input distances or the metric that captures the relevant geometric features of the data is a key step in many machine learning algorithms.
Unsupervised Metric Learning in Presence of Missing Data
Here, we present a new algorithm MR-MISSING that extends these previous algorithms and can be used to compute low dimensional representation on data sets with missing entries.
