Dimensionality reduction is a popular strategy for studying complex dynamical systems, especially high-dimensional networked systems. Its goal to discover more compact version of the system that retains almost important characteristics original and offers insights into inner functioning as well long-term behavior. However, because interaction structures, parameter configurations, initial condit...

Abstract Existing methods for explaining black box learning models often focus on building local explanations of the models’ behaviour particular data items. It is possible to create global all items, but these generally have low fidelity complex models. We propose a new supervised manifold visualisation method, slisemap , that simultaneously finds items and builds (typically) two-dimensional m...

Generalization of time series prediction remains an important open issue in machine learning, wherein earlier methods have either large generalization error or local minima. We develop analytically solvable, unsupervised learning scheme that extracts the most informative components for predicting future inputs, termed predictive principal component analysis (PredPCA). Our can effectively remove...

Dimensionality reduction (DR) of data is a crucial issue for many machine learning tasks, such as pattern recognition and classification. In this paper, we present quantum algorithm circuit to efficiently perform linear discriminant analysis (LDA) dimensionality reduction. Firstly, the presented improves existing LDA avoid error caused by irreversibility between-class scatter matrix $S_B$ in or...