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Publications [#243806] of Mauro Maggioni

Papers Published

  1. Little, AV; Jung, YM; Maggioni, M, Multiscale estimation of intrinsic dimensionality of data sets, Aaai Fall Symposium Technical Report, vol. FS-09-04 (December, 2009), pp. 26-33
    (last updated on 2019/02/19)

    We present a novel approach for estimating the intrinsic dimensionality of certain point clouds: we assume that the points are sampled from a manifold M of dimension k, with k ≪ D, and corrupted by D-dimensional noise. When M is linear, one may analyze this situation by SVD: with no noise one would obtain a rank k matrix, and noise may be treated as a perturbation of the covariance matrix. When M is a nonlinear manifold, global SVD may dramatically overestimate the intrinsic dimensionality. We introduce a multiscale version SVD and discuss how one can extract estimators for the intrinsic dimensionality that are highly robust to noise, while require a smaller sample size than current estimators. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.
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