Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2011). Approximate Convex Hulls Family for One-Class Cassification. In Carlo Sansone, Josef Kittler, & Fabio Roli (Eds.), 10th International Workshop on Multiple Classifier Systems (Vol. 6713, pp. 106–115). LNCS. Springer Berlin Heidelberg.
Abstract: In this work, a new method for one-class classification based on the Convex Hull geometric structure is proposed. The new method creates a family of convex hulls able to fit the geometrical shape of the training points. The increased computational cost due to the creation of the convex hull in multiple dimensions is circumvented using random projections. This provides an approximation of the original structure with multiple bi-dimensional views. In the projection planes, a mechanism for noisy points rejection has also been elaborated and evaluated. Results show that the approach performs considerably well with respect to the state the art in one-class classification.
|
Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2014). Approximate polytope ensemble for one-class classification. PR - Pattern Recognition, 47(2), 854–864.
Abstract: In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets.
Keywords: One-class classification; Convex hull; High-dimensionality; Random projections; Ensemble learning
|
Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2010). Classyfing Agitation in Sedated ICU Patients. In Medical Image Computing in Catalunya: Graduate Student Workshop (19–20).
Abstract: Agitation is a serious problem in sedated intensive care unit (ICU) patients. In this work, standard machine learning techniques working on wearable accelerometer data have been used to classifying agitation levels achieving very good classification performances.
|
|
Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2010). Embedding Random Projections in Regularized Gradient Boosting Machines. In Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (44–53).
|
Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2009). Face-to-face social activity detection using data collected with a wearable device. In 4th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 5524, 56–63). LNCS. Springer Berlin Heidelberg.
Abstract: In this work the feasibility of building a socially aware badge that learns from user activities is explored. A wearable multisensor device has been prototyped for collecting data about user movements and photos of the environment where the user acts. Using motion data, speaking and other activities have been classified. Images have been analysed in order to complement motion data and help for the detection of social behaviours. A face detector and an activity classifier are both used for detecting if users have a social activity in the time they worn the device. Good results encourage the improvement of the system at both hardware and software level
|
Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2011). Human Activity Recognition from Accelerometer Data using a Wearable Device. In J. Vitria, J. M. R. Sanches, & M. Hernández (Eds.), 5th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 6669, pp. 289–296). LNCS. Springer Berlin Heidelberg.
Abstract: Activity Recognition is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Recently, recognizing everyday life activities becomes one of the challenges for pervasive computing. In our work, we developed a novel wearable system easy to use and comfortable to bring. Our wearable system is based on a new set of 20 computationally efficient features and the Random Forest classifier. We obtain very encouraging results with classification accuracy of human activities recognition of up to 94%.
|
Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2012). Personalization and User Verification in Wearable Systems using Biometric Walking Patterns. PUC - Personal and Ubiquitous Computing, 16(5), 563–580.
Abstract: In this article, a novel technique for user’s authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a ‘wild’ scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies.
|
|
Pierluigi Casale, Oriol Pujol, & Petia Radeva. (2011). User Verification From Walking Activity. First Steps Towards a Personal Verification System. In 1st International Conference on Pervasive and Embedded Computing and Communication Systems.
|
