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Found 3 papers, 2 papers with code
On Projections to Linear Subspaces
The merit of projecting data onto linear subspaces is well known from, e. g., dimension reduction.
MESS: Manifold Embedding Motivated Super Sampling
Many approaches in the field of machine learning and data analysis rely on the assumption that the observed data lies on lower-dimensional manifolds.
ABID: Angle Based Intrinsic Dimensionality
In this paper we introduce an orthogonal concept, which does not use any distances: we use the distribution of angles between neighbor points.
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