Disguised Discrimination of Locality-Based Unsupervised Dimensionality Reduction

: Many locality-based unsupervised dimensionality reduction (DR) algorithms have recently been proposed and demonstrated to be effective to a certain degree in some classification tasks. In this paper, we aim to show that: 1) a discriminant disposal is intentionally or unintentionally induced from the construction of locality in these unsupervised algorithms, however, such a discrimination is often inconsistent with the actual class information, so here called disguised discrimination; 2) sensitivities of these algorithms to local neighbor parameters stem from the inconsistency between the disguised discrimination and the actual class information; 3) how such inconsistency impacts the classification performance of these algorithms. The experiments on the benchmark face datasets testify our statements that are expected to provide some insight into the unsupervised leaning based on locality.

• Type: Journal
• Year: 2010
• Where: IJPRAI
• Authors: Bo Yang, Songcan Chen
• Keywords: Actual Class Information, Algorithms, Disguised Discrimination, IJPRAI 2010