Metric learning for sequences in relational LVQ

Mokbel B, Paaßen B, Schleif F-M, Hammer B (2015)
Neurocomputing 169(SI): 306-322.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
OA
Abstract / Bemerkung
Metric learning constitutes a well-investigated field for vectorial data with successful applications, e.g. in computer vision, information retrieval, or bioinformatics. One particularly promising approach is offered by low-rank metric adaptation integrated into modern variants of learning vector quantization (LVQ). This technique is scalable with respect to both data dimensionality and the number of data points, and it can be accompanied by strong guarantees of learning theory. Recent extensions of LVQ to general (dis-)similarity data have paved the way towards LVQ classifiers for non-vectorial, possibly discrete, structured objects such as sequences, which are addressed by classical alignment in bioinformatics applications. In this context, the choice of metric parameters plays a crucial role for the result, just as it does in the vectorial setting. In this contribution, we propose a metric learning scheme which allows for an autonomous learning of parameters (such as the underlying scoring matrix in sequence alignments) according to a given discriminative task in relational LVQ. Besides facilitating the often crucial and problematic choice of the scoring parameters in applications, this extension offers an increased interpretability of the results by pointing out structural invariances for the given task.
Stichworte
Sequential data; Relational LVQ; Metric learning; Dissimilarity data
Erscheinungsjahr
2015
Zeitschriftentitel
Neurocomputing
Band
169
Ausgabe
SI
Seite(n)
306-322
ISSN
0925-2312
Page URI
https://pub.uni-bielefeld.de/record/2710031

Zitieren

Mokbel B, Paaßen B, Schleif F-M, Hammer B. Metric learning for sequences in relational LVQ. Neurocomputing. 2015;169(SI):306-322.
Mokbel, B., Paaßen, B., Schleif, F. - M., & Hammer, B. (2015). Metric learning for sequences in relational LVQ. Neurocomputing, 169(SI), 306-322. doi:10.1016/j.neucom.2014.11.082
Mokbel, B., Paaßen, B., Schleif, F. - M., and Hammer, B. (2015). Metric learning for sequences in relational LVQ. Neurocomputing 169, 306-322.
Mokbel, B., et al., 2015. Metric learning for sequences in relational LVQ. Neurocomputing, 169(SI), p 306-322.
B. Mokbel, et al., “Metric learning for sequences in relational LVQ”, Neurocomputing, vol. 169, 2015, pp. 306-322.
Mokbel, B., Paaßen, B., Schleif, F.-M., Hammer, B.: Metric learning for sequences in relational LVQ. Neurocomputing. 169, 306-322 (2015).
Mokbel, Bassam, Paaßen, Benjamin, Schleif, Frank-Michael, and Hammer, Barbara. “Metric learning for sequences in relational LVQ”. Neurocomputing 169.SI (2015): 306-322.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Volltext(e)
Access Level
OA Open Access
Zuletzt Hochgeladen
2019-09-25T06:36:43Z
MD5 Prüfsumme
c9f1488f89455be17f19414433dcdce1

Link(s) zu Volltext(en)
Access Level
Restricted Closed Access
Material in PUB:
Zitiert
Java Sorting Programs
Paaßen B (2016)
Bielefeld University.