Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent

We propose a new two stage algorithm LING for large scale regression problems. LING has the same risk as the well known Ridge Regression under the fixed design setting and can be computed much faster. Our experiments have shown that LING performs well in terms of both prediction accuracy and computational efficiency compared with other large scale regression algorithms like Gradient Descent, Stochastic Gradient Descent and Principal Component Regression on both simulated and real datasets

Total embedding distributions of Ringel ladders

The total embedding distributions of a graph is consisted of the orientable embeddings and non- orientable embeddings and have been know for few classes of graphs. The genus distribution of Ringel ladders is determined in [Discrete Mathematics 216 (2000) 235-252] by E.H. Tesar. In this paper, the explicit formula for non-orientable embeddings of Ringel ladders is obtained

Fast Linear Algorithms for Machine Learning

Nowadays linear methods like Regression, Principal Component Analysis and Canoni- cal Correlation Analysis are well understood and widely used by the machine learning community for predictive modeling and feature generation. Generally speaking, all these methods aim at capturing interesting subspaces in the original high dimensional feature space. Due to the simple linear structures, these methods all have a closed form solution which makes computation and theoretical analysis very easy for small datasets. However, in modern machine learning problems it\u27s very common for a dataset to have millions or billions of features and samples. In these cases, pursuing the closed form solution for these linear methods can be extremely slow since it requires multiplying two huge matrices and computing inverse, inverse square root, QR decomposition or Singular Value Decomposition (SVD) of huge matrices. In this thesis, we consider three fast al- gorithms for computing Regression and Canonical Correlation Analysis approximate for huge datasets