It has been shown that dimension reduction methods such as Principal Component Analysis (PCA) may be inherently prone to unfairness and treat data from different sensitive groups race, color, sex, etc., unfairly. In pursuit of fairness-enhancing dimensionality reduction, using the notion Pareto optimality, we propose an adaptive first-order algorithm learn a subspace preserves fairness, while s...

In this paper we present a method for an appearance-based modeling of the environment of a mobile robot. We describe the task (localization of the robot) in a probabilistic framework. Linear image features are extracted using a Principal Component Analysis. The appearance model is represented as a probability density function of the image feature vector given the location of the robot. We estim...

Edge geometric measurement analysis is an important method of image understanding and portraying the target feature. In this paper, we compress 17 interrelated shape descriptors which are based on edge geometric measure into 6 independent components, and discuss their meanings by using principal component analysis. The analyses in this article provide guidance for the shape feature optimization...

The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN  of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most      relevant information of X. These eigenvectors are called principal components [8]. Ass...