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]. assume that n images in a set are originally represented in mat...

This essay addresses some of the critical and cohesive teaching philosophies regarding finance theory education in an effort to rekindle and increase our awareness thereof in the wake of rapid advancement and propagation of technology-based practical skills training, which appears to increasingly discount the usefulness of classroom-taught theories. Recognizing the significance of fostering pro...

Grounded in the 2 x 2 achievement goal framework (Elliot & McGregor, 2001), the purpose of this study was to investigate the temporal relationships between achievement goals, competition appraisals and indices of psychological and emotional welfare among elite adolescent soccer players. A subsidiary aim was to ascertain the mediational role of competition appraisals in explaining the potential ...

In this lecture, we bring money into the picture, while still thinking about a “matching” like problem. Suppose we have: 1. We have m goods G for sale 2. n buyers i who each have valuation functions over bundles, vi : 2 G → [0, 1]. Buyers have quasi-linear utility functions, which means they can (linearly) trade off their value for goods and their value for money. If each good j ∈ G has a price...

In this lecture, we bring money into the picture, while still thinking about a “matching” like problem. Suppose we have: 1. We have m goods G for sale 2. n buyers i who each have valuation functions over bundles, vi : 2 G → [0, 1]. Buyers have quasi-linear utility functions, which means they can (linearly) trade off their value for goods and their value for money. If each good j ∈ G has a price...