Authors:
Josué Melka
and
Jean-Jacques Mariage
Affiliation:
Laboratoire d’Informatique Avancée de Saint-Denis and Université Paris 8, France
Keyword(s):
Neural-based Data-mining, Self-Organizing Map Learning Algorithm, Complex Information Processing, Parallel Implementation, Sparse Vectors.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Self-Organization and Emergence
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Neural-based learning algorithms, which in most cases implement a lengthy iterative convergence procedure, are often hardly adapted to very sparse input data, both due to practical issues concerning time and memory usage, and to the inherent difficulty of learning in high dimensional space. However, the description of many real-world data sets is sparse by nature, and learning algorithms must circumvent this barrier. This paper proposes adaptations of the standard and the batch versions of the Self-Organizing Map algorithm, specifically fine-tuned for high dimensional sparse data, with parallel implementation efficiency in mind. We extensively evaluate the performance of both adaptations on a set of experiments carried out on several real and artificial large benchmark datasets of sparse format from the LIBSVM Data: Classification. Results show that our approach brings a significant improvement in execution time.