When dealing with datasets comprising high‐dimensional points, it is usually advantageous
to discover some data structure. A fundamental information needed to this aim is the …

Nonasymptotic rates for manifold, tangent space and curvature estimation Page 1 The Annals
of Statistics 2019, Vol. 47, No. 1, 177–204 https://doi.org/10.1214/18-AOS1685 © Institute of …

We present a breadth-oriented collection of cross-platform command-line tools for
researchers in machine learning called Waffles. The Waffles tools are designed to offer a …

Manifold learning is a class of machine learning methods that exploits the observation that
high-dimensional data tend to lie on a smooth lower-dimensional manifold. Manifold …

Let MM be an m-dimensional smooth compact manifold embedded in R^ d R d, where m is a
constant known to us. Suppose that a dense set of points are sampled from MM according to …

Though a great deal of research work has been devoted to the development of
dimensionality reduction algorithms, the problem is still open. The most recent and effective …

Some datasets exhibit non-trivial geometric or topological features that can be interesting to
infer. This thesis deals with non-asymptotic rates for various geometric quantities associated …

The primary objective of the study involved the identification and quantification of systematic
errors associated with the measurement of sacral obliquity on radiographic images. A …

Significant characteristics of cloud computing such as elasticity, scalability and payment
model attract businesses to replace their legacy infrastructure with the newly offered cloud …

Data that is represented with high dimensionality presents a computational complexity
challenge for many existing algorithms. Limiting dimensionality by discarding attributes is …