Techniques based on Bag Of Words approach represent images by quantizing local descriptors and summarizing their distribution in a histogram. Differently, in this paper we describe an image as multivariate Gaussian distribution, estimated over the extracted local descriptors. The estimated distribution is mapped to a high-dimensional descriptor, by concatenating the mean vector and the projecti...

Multivariate sequence mining and classification are important and challenging tasks. They can be applied to numerous domains including medical diagnosis, handwriting deficiency diagnosis, identification of users for security or personalized TV services, and even transportation and traffic planning. The problem we address in this dissertation is classification of multivariate sequences. Multivar...

The misclassification error which is usually used in tests to compare classification algorithms, does not make a distinction between the sources of error, namely, false positives and false negatives. Instead of summing these in a single number, we propose to collect multivariate statistics and use multivariate tests on them. Information retrieval uses the measures of precision and recall, and s...

Characterizing how activity in the central and autonomic nervous systems corresponds to distinct emotional states is one of the central goals of affective neuroscience. Despite the ease with which individuals label their own experiences, identifying specific autonomic and neural markers of emotions remains a challenge. Here we explore how multivariate pattern classification approaches offer an ...

The increase in the number of complex temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data. This work focuses on methods for multivariate time-series classification. Time series classification is a challenging problem mostly because the number of temporal features that describe the data and a...