4 research outputs found
Ranked centroid projection: A data visualization approach based on self-organizing maps
The Self-Organizing Map (SOM) is an unsupervised neural network model that provides topology-preserving mapping from high-dimensional input spaces onto a commonly two-dimensional output space. In this study, the clustering and visualization capabilities of the SOM, especially in the analysis of textual data, i.e. document collections, are reviewed and further developed. A novel clustering and visualization approach based on the SOM is proposed for the task of text data mining. The proposed approach first transforms the document space into a multi-dimensional vector space by means of document encoding. Then a growing hierarchical SOM (GHSOM) is trained and used as a baseline framework, which automatically produces maps with various levels of details. Following the training of the GHSOM, a novel projection method, namely the Ranked Centroid Projection (RCP), is applied to project the input vectors onto a hierarchy of two-dimensional output maps. The projection of the input vectors is treated as a vector interpolation into a two-dimensional regular map grid. A ranking scheme is introduced to select the nearest R units around the input vector in the original data space, the positions of which will be taken into account in computing the projection coordinates.The proposed approach can be used both as a data analysis tool and as a direct interface to the data. Its applicability has been demonstrated in this study using an illustrative data set and two real-world document clustering tasks, i.e. the SOM paper collection and the Anthrax paper collection. Based on the proposed approach, a software toolbox is designed for analyzing and visualizing document collections, which provides a user-friendly interface and several exploration and analysis functions.The presented SOM-based approach incorporates several unique features, such as the adaptive structure, the hierarchical training, the automatic parameter adjustment and the incremental clustering. Its advantages include the ability to convey a large amount of information in a limited space with comparatively low computation load, the potential to reveal conceptual relationships among documents, and the facilitation of perceptual inferences on both inter-cluster and within-cluster relationships
Novel neural approaches to data topology analysis and telemedicine
1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz
Computer aided identification of biological specimens using self-organizing maps
For scientific or socio-economic reasons it is often necessary or desirable that biological material be identified. Given that there are an estimated 10 million living organisms on Earth, the identification of biological material can be problematic. Consequently the services of taxonomist specialists are often required. However, if such expertise is not readily available it is necessary to attempt an identification using an alternative method. Some of these alternative methods are unsatisfactory or can lead to a wrong identification. One of the most common problems encountered when identifying specimens is that important diagnostic features are often not easily observed, or may even be completely absent. A number of techniques can be used to try to overcome this problem, one of which, the Self Organizing Map (or SOM), is a particularly appealing technique because of its ability to handle missing data. This thesis explores the use of SOMs as a technique for the identification of indigenous trees of the Acacia species in KwaZulu-Natal, South Africa. The ability of the SOM technique to perform exploratory data analysis through data clustering is utilized and assessed, as is its usefulness for visualizing the results of the analysis of numerical, multivariate botanical data sets. The SOM’s ability to investigate, discover and interpret relationships within these data sets is examined, and the technique’s ability to identify tree species successfully is tested. These data sets are also tested using the C5 and CN2 classification techniques. Results from both these techniques are compared with the results obtained by using a SOM commercial package. These results indicate that the application of the SOM to the problem of biological identification could provide the start of the long-awaited breakthrough in computerized identification that biologists have eagerly been seeking.Dissertation (MSc)--University of Pretoria, 2011.Computer Scienceunrestricte
Reconnaissance des actions humaines : méthode basée sur la réduction de dimensionnalité par MDS spatio-temporelle
L’action humaine dans une séquence vidéo peut être considérée comme un volume spatio-
temporel induit par la concaténation de silhouettes dans le temps. Nous présentons une
approche spatio-temporelle pour la reconnaissance d’actions humaines qui exploite des
caractéristiques globales générées par la technique de réduction de dimensionnalité MDS
et un découpage en sous-blocs afin de modéliser la dynamique des actions. L’objectif
est de fournir une méthode à la fois simple, peu dispendieuse et robuste permettant la
reconnaissance d’actions simples. Le procédé est rapide, ne nécessite aucun alignement
de vidéo, et est applicable à de nombreux scénarios. En outre, nous démontrons la
robustesse de notre méthode face aux occultations partielles, aux déformations de
formes, aux changements d’échelle et d’angles de vue, aux irrégularités dans l’exécution
d’une action, et à une faible résolution.Human action in a video sequence can be seen as a space-time volume induced by the
concatenation of silhouettes in time. We present a space-time approach for human
action recognition, which exploits global characteristics generated by the technique
of dimensionality reduction MDS and a cube division into sub-blocks to model the
dynamics of the actions. The objective is to provide a method that is simple, inexpensive
and robust allowing simple action recognition. The process is fast, does not require
video alignment, and is applicable in many scenarios. Moreover, we demonstrate
the robustness of our method to partial occlusion, deformation of shapes, significant
changes in scale and viewpoint, irregularities in the performance of an action, and
low-quality video