Deflated manifold embedding PCA framework via multiple instance factorings

doi:10.1007/s11042-020-09789-3

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Deflated manifold embedding PCA framework via multiple instance factorings

2020-09-24

PRE

Ernest Domanaanmwi Ganaa

Xiang‐Jun Shen *

Timothy Apasiba Abeo

DOI：10.1007/s11042-020-09789-3

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Abstract

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Principal component analysis is a widely used technique. However, it is sensitive to noise and considers data samples to be linearly distributed globally. To tackle these challenges, a novel technique robust to noise termed deflated manifold embedding PCA is proposed. In this framework, we unify PCA with manifold embedding to preserve both global and local geometric structures of linear and non-linear data in sub-manifolds. Additionally, a scaling-factor is imposed in the instance space to mitigate the impact of noise in pursuing projections. By using cosine similarity and total distance approaches, we iteratively learn the relationships between instances and projections in order to discriminate between authentic and corrupt instances. Further, a deflation technique is applied to establish multi-relationships between instances and every pursued projection for thorough discrimination. Experimental evaluation of the proposed methods on five datasets show great improvements in their performances over six state-of-the-art techniques.

Keywords:

Principal component analysis

Manifold embedding

Dimension reduction

Deflation

Instance factorings

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