Archive

Independent component analysis (ICA) is a blind source separation technique that allows the separation of linear mixtures of signals into maximal statistically independent sources, normally called independent components (ICs). This technique relies on several mathematical assumptions which need to be met by the signals of interest. In the field of neurophysiologic signals ICA has been shown to be successful in disentangling multi-channel electroencephalogram (EEG) recordings into...

In this presentation I will describe the key cross-language annotation guidelines to provide support for state-of-the art machine translation systems. The guidelines aim at improving the quality of the statistical machine translation output by using linguistically-informed and motivated annotation of special case multiwords and semantico-syntactic translation units. The guidelines were based on the alignment of bilingual texts of the common test set of the Europarl...

Subcellular location is an important property of proteins, carefully regulated by the cell machinery. To determine subcellular location on a proteome-wide scale, fluorescent image data is most commonly used and a classification system is employed for analysis. These systems assign each protein to one of a small set of predefined location classes (typically the major organelles). Too often, in the past, the performance of classification was...

The problem of cross-modal retrieval from multimedia repositories is considered. This problem addresses the design of retrieval systems that support queries across content modalities, e.g. using text to search for images. A mathematical formulation is proposed, equating the design of cross-modal retrieval systems to that of isomorphic feature spaces for different content modalities. Two hypotheses are investigated, regarding the fundamental attributes of these spaces. The...

In this talk we are interested in distributed algorithms for solving separable optimization problems. Many problems in engineering can be formulated as separable optimization problems, i.e., minimizing the sum of P functions subject to the intersection of P sets. Our goal is to solve such problem when the P functions and sets are not known at a single location, but rather distributed across a network...

High-dimensional datasets are increasingly common in learning problems, in many different domains, such as text categorization, genomics, econometrics, and computer vision. The excessive number of features carries the problem of memory usage in order to represent and deal with these datasets, clearly showing the need for adequate methods for feature representation, reduction, and selection, to both improve the classification accuracy and the memory requirements for...

The patterns in collections of real world objects are often not based on a limited set of isolated properties such as features. Instead, the totality of their appearance constitutes the basis of the human recognition of patterns. Structural pattern recognition aims to find explicit procedures that mimic the learning and classification made by human experts in well-defined and restricted areas of application. This is often done...

In this presentation we develop a dynamic continuous solution to the clustering problem of data characterized by a mixture of K distributions, where K is given a priori. The proposed solution resorts to game theory tools, in particular mean field games and can be interpreted as the continuous version of a generalized Expectation-Maximization (GEM) algorithm. The main contributions of this paper are twofold: first, we...

Speech enhancement techniques aim to recover the original clean signal underlying corrupted speech. Such techniques typically operate in the short-time Fourier transform (STFT) domain where phenomena like additivity of background noises, interfering speakers and echoes are easier to model. By contrast, automatic speech recognition (ASR), and in general most speech-related machine learning applications, operate on feature spaces that are non-linear transformations of the STFT. The...

How to detect and classify credit card fraud? What is fraud for a person, say, buying expensive jewelry, spending on online casinos or simply spending a lot of money, is just the regular day-to-day for another. To make matters worse, the same people behave differently with different cards (personal card vs company card) and in different moments ("home mode" vs "vacation mode"), there are many...