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...

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...

In the tube drawing process, there are a bunch of parameters which play key role in process performance. Thus, finding the optimized parameters is a controversial issue. Current study aimed to produce a squared section of round tube by tube sinking process. To simulate the process finite element method (FEM) was used. Then, to find a meaningful kinship between process input and output parameter...

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