Generalized Relevance LVQ with Correlation Measures for Biological Data
نویسندگان
چکیده
Generalized Relevance Learning Vector Quantization (GRLVQ) is combined with correlation-based similarity measures. These are derived from the Pearson correlation coefficient in order to replace the adaptive squared Euclidean distance which is typically used for GRLVQ. Patterns can thus be used without further preprocessing and compared in a manner invariant to data shifting and scaling transforms. High accuracies are demonstrated for a reference experiment of handwritten character recognition and good discrimination ability is shown for the detection of systematic differences between gene expression experiments.
منابع مشابه
Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis
A correlation-based similarity measure is derived for generalized relevance learning vector quantization (GRLVQ). The resulting GRLVQ-C classifier makes Pearson correlation available in a classification cost framework where data prototypes and global attribute weighting terms are adapted into directions of minimum cost function values. In contrast to the Euclidean metric, the Pearson correlatio...
متن کاملGeneralized Learning Graph Quantization
This contribution extends generalized LVQ, generalized relevance LVQ, and robust soft LVQ to the graph domain. The proposed approaches are based on the basic learning graph quantization (lgq) algorithm using the orbifold framework. Experiments on three data sets show that the proposed approaches outperform lgq and lgq2.1.
متن کاملAdvanced metric adaptation in Generalized LVQ for classification of mass spectrometry data
Metric adaptation constitutes a powerful approach to improve the performance of prototype based classication schemes. We apply extensions of Generalized LVQ based on different adaptive distance measures in the domain of clinical proteomics. The Euclidean distance in GLVQ is extended by adaptive relevance vectors and matrices of global or local influence where training follows a stochastic gradi...
متن کاملRobust object segmentation by adaptive metrics in Generalized LVQ
We investigate the effect of several adaptive metrics in the context of figure-ground segregation, using Generalized LVQ to train a classifier for image regions. Extending the Euclidean metrics towards local matrices of relevance-factors does not only lead to a higher classification accuracy and increased robustness on heterogeneous/noisy data, but also figureground segregation using this adapt...
متن کاملRegularization in matrix learning
We present a regularization technique to extend recently proposed matrix learning schemes in Learning Vector Quantization (LVQ). These learning algorithms extend the concept of adaptive distance measures in LVQ to the use of relevance matrices. In general, metric learning can display a tendency towards over-simplification in the course of training. An overly pronounced elimination of dimensions...
متن کامل