Time series analysis is a promising approach to discover temporal patterns from time stamped, numeric data. A novel approach to apply time series analysis to discern temporal info...
Harvey P. Siy, Parvathi Chundi, Daniel J. Rosenkra...
The analysis of the runtime behavior of a software system yields vast amounts of information, making accurate interpretations difficult. Filtering or compression techniques are o...
kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...
—Networks are widely used in modeling relational data often comprised of thousands of nodes and edges. This kind of data alone implies a challenge for its visualization as it is ...
We develop a supervised dimensionality reduction method, called Lorentzian Discriminant Projection (LDP), for feature extraction and classification. Our method represents the str...