Parallelized Tensor Train Learning of Polynomial Classifiers

Pattern classification is the machine learning task of identifying to which category a new observation belongs, on the basis of a training set of observations whose category membership is known. This type of machine learning algorithm that uses a known training dataset to make predictions is called supervised learning, which has been extensively studied and has wide applications in the fields of bioinformatics [1], computer-aided diagnosis (CAD) [2], machine vision [3], speech recognition [4], handwriting recognition [5], spam detection and many others [6], [7], [8]. Usually, different kinds of learning methods use different models to generalize from training examples to novel test examples. As pointed out in [9], [10], one of the important invariants in these applications is the local structure: variables that are spatially or temporally nearby are highly correlated. Local correlations benefit extracting local features because configurations of neighboring variables can be classified into a small number of categories (e.g.