Perception preserving projections

Abstract

Linear projection for reducing data dimensionality is a common practice in various data processing applications. Among the existing projection methods, Principal Component Analysis (PCA) is arguably the most popular one. Standard PCA used in image preprocessing pursues the projection directions by minimizing the reconstruction error in a least square sense. However, since PCA does not adapt to the data or any specific domains, it may lead to severe loss of certain discriminative features during the projection, and damage the performance of either human perception (e.g. stimulus in the visual cortex, as modeled by Gabor wavelets), or machine perceptions (e.g. recognizing the images based on a certain type of visual features), or both. In this paper, we propose a novel Perception Preserving Projections (PPP) method to preserve the information for specific perception systems. In particular, PPP incorporates domain-specific feature extractor into the standard PCA formulation for the projection learning procedure. This enables PPP to make more sensible projections for feature based perception systems while retaining the simplicity and unsupervised manner of PCA. In experimental studies, PPP shows clear effectiveness and improvement over PCA in terms of two performance metrics: feature extraction deviation and the pattern recognition accuracy.