Publication

Streaming Principal Component Analysis From Incomplete Data

Armin Eftekhari
2019
Journal Articles

Abstract

Linear subspace models are pervasive in computational sciences and particularly used for large datasets which are often incomplete due to privacy issues or sampling constraints. Therefore, a critical problem is developing an efficient algorithm for detecting low-dimensional linear structure from incomplete data efficiently, in terms of both computational complexity and storage.

Official source

https://infoscience.epfl.ch/entities/publication/3175fcbc-f23d-46fa-bd51-eedd5a9cbc0a

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Armin Eftekhari
2019
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/3175fcbc-f23d-46fa-bd51-eedd5a9cbc0a

About this result

Related concepts (19)

Nonlinear dimensionality reduction

Nonlinear dimensionality reduction, also known as manifold learning, refers to various related techniques that aim to project high-dimensional data onto lower-dimensional latent manifolds, with the goal of either visualizing the data in the low-dimensional space, or learning the mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa) itself. The techniques described below can be understood as generalizations of linear decomposition methods used for dimensionality reduction, such as singular value decomposition and principal component analysis.

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In functional analysis and related areas of mathematics, a continuous linear operator or continuous linear mapping is a continuous linear transformation between topological vector spaces. An operator between two normed spaces is a bounded linear operator if and only if it is a continuous linear operator. Continuous function (topology) and Discontinuous linear map Bounded operator Suppose that is a linear operator between two topological vector spaces (TVSs). The following are equivalent: is continuous.

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