Large Scale Manifold Learning
نویسنده
چکیده
In Non-Linear Dimensionality Reduction (NLDR) algorithms, one is given a collection of N objects, each a vector in RD, and the objective is to find an embedding in a low-dimensional Euclidean space Rd while preserving the geometry as faithfully as possible. In traditional NLDR methods like classical MDS, space complexity turns out to be O(N2) which for large N becomes unaffordable with reasonable memory. Here we wish to maintain the space complexity of the problem O(N).
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