Publication

Adaptive basis functions for prototype-based classification of functional data

Bani, G., Seiffert, U., Biehl, M. & Melchert, F. 31-Aug-2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). IEEEXplore, p. 1-8 8 p.

Research output: Scientific - peer-reviewConference contribution

Copy link to clipboard

Documents

  • Adaptive Basis Functions for Prototype-based

    Final publisher's version, 453 KB, PDF-document

DOI

We present a framework for distance-based classification of functional data. We consider the analysis of labeled spectral data and time series by means of Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) as an example. To take advantage of the functional nature a functional expansion of the input data is considered. Instead of using a predefined set of basis functions for the expansion a more flexible scheme of an adaptive functional basis is employed. GMLVQ is applied on the resulting functional parameters to solve the classification task. For comparison of the classification a GMLVQ system is also applied to the raw input data, as well as on data expanded by a different predefined functional basis. Computer experiments show that the methods offers potential to improve classification performance significantly. Furthermore the analysis of the adapted set of basis functions give further insights into the data structure and yields an option for a drastic reduction of dimensionality.
Original languageEnglish
Title of host publication12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)
PublisherIEEEXplore
Pages1-8
Number of pages8
ISBN (Electronic)978-1-5090-6638-4
StatePublished - 31-Aug-2017
Event12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) - Nancy, France
Duration: 28-Jun-201730-Jun-2017

Conference

Conference12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)
CountryFrance
CityNancy
Period28/06/201730/06/2017

Event

12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)

28/06/201730/06/2017

Nancy, France

Event: Conference

    Keywords

  • Benchmark testing, Chebyshev approximation, Optimization, Prototypes, Time series analysis, Training, Vector quantization
Related Activities
  1. Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization

    Biehl, M. (Participant), Melchert, F. (Speaker), Michael LeKander (Speaker), Michiel Straat (Speaker)
    28-Jun-201730-Jun-2017

    Activity: ScientificParticipation in conference

View all (1) »

View graph of relations

ID: 47348077