TY  - JOUR
AB  - 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.
DA  - 2015
DO  - 10.1016/j.neucom.2014.11.082
KW  - Sequential data
KW  - Relational LVQ
KW  - Metric learning
KW  - Dissimilarity data
LA  - eng
IS  - SI
M2  - 306
PY  - 2015
SN  - 0925-2312
SP  - 306-322
T2  - Neurocomputing
TI  - Metric learning for sequences in relational LVQ
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-27100312
Y2  - 2024-11-22T04:59:36
ER  -