Visualising data for exploratory analysis is a major challenge in many applications where there is a need to gain insight into the structure and distribution of the data (e.g. to find common patterns and to identify relationships between samples as well as variables). Typically, visualisationmethods like principal components analysis (PCA) and multi-dimensional scaling (MDS) are employed. These...

A Universal Compression scheme is presented, to compress sequences of quantum information from unknown quantum sources (i.e. described by unknown density matrix ρ) asymptotically to S(ρ), with fidelity bounded toward unity. The introduction of a“B-diagonalisation” process allows us to treat the input as if it were diagonal in a known basis B. Applying a version of the Lempel-Ziv algorithm in th...

The first step in the spatial normalization of brain images is usually to determine the affine transformation that best maps the image to a template image in a standard space. We have developed a rapid and automatic method for performing this registration, which uses a Bayesian scheme to incorporate prior knowledge of the variability in the shape and size of heads. We compared affine registrati...

This paper presents a new approach for human walking modeling from monocular image sequences. A kinematics model and a walking motion model are introduced in order to exploit prior knowledge. The proposed technique consists of two steps. Initially, an efficient feature point selection and tracking approach is used to compute feature points’ trajectories. Peaks and valleys of these trajectories ...

Pedestrian detection is a classical and hot issue in object detection. Many approaches have been proposed in this area. However, it remains a challenging problem due to the variances in lighting conditions, scene structures, clothes, view angles, postures, scales, occlusions, etc. Previous survey [1] has summarized that using better features plays an important role in improving detection qualit...

A study of the combined influence of prior knowledge and stimulus dimensionality on category learning was conducted. Subjects learned category structures with the same number of necessary dimensions but with more or fewer additional, redundant dimensions and with either knowledge-related or knowledge-unrelated features. Minimal-learning models predict that all subjects, regardless of condition,...

Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the issue, several knowledge-based models have been proposed to incorporate prior knowledge provided by the user to guide modeling. In this paper, we take a major step forward and show that in the big data...

Microarrays allow monitoring of thousands of genes over time periods. Recently, gene clustering approaches specially adapted to deal with the time dependences of these data have been proposed. According to these methods, we investigate here how to use prior knowledge about the approximate profile of some classes to improve the classification result. We propose a Bayesian approach to this proble...

Increasing demand for digitalization of Electronic Health Records results in increased demand for effective data mining solutions. In this study we enhance the classical Support Vector Machine Recursive Feature Elimination (SVM-RFE) approach to optimally estimate disease risk from hospital discharge record data. Our approach is based on incorporating prior knowledge from human disease networks ...

Gaussian processes (GP) models form an emerging methodology for modelling nonlinear dynamic systems which tries to overcome certain limitations inherent to traditional methods such as e.g. neural networks, fuzzy models or local model networks. The GP model seems promising for three reasons — first, smaller number of training parameters, second, the variance of model’s output is automatically ob...