Adaptive Relevance Matrices in Learning Vector Quantization
Adaptive Relevance Matrices in Learning Vector Quantization
复制标题
DOI：
10.1162/neco.2009.11-08-908
复制
发表时间：
2009-12-01
影响因子：
2.9
通讯作者：
Hammer, Barbara
中科院分区：
文献类型：
--
作者：
Schneider, Petra;Biehl, Michael;Hammer, Barbara
我们提出了一个新的矩阵学习方案，扩展相关学习矢量量化（RLVQ），一个有效的基于原型的分类算法，对一般的自适应度量。通过在距离度量中引入完整的相关性因子矩阵，可以考虑并自动化不同特征之间的相关性及其对分类方案的重要性，并且在训练期间进行一般度量适应。与RLVQ及其变体中使用的加权欧几里得度量相比，全矩阵更能恰当地表示数据的内部结构。大的边际泛化边界可以转移到这种情况下，导致边界是独立的输入维度。这也适用于附加到每个原型的局部度量，其对应于分段二次决策边界。该算法进行了测试比较，替代学习矢量量化方案使用人工数据集，一个基准的多类问题，从UCI库，和一个问题，从生物信息学，剪接位点的识别C。优美的
We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account and automated, and general metric adaptation takes place during training. In comparison to the weighted Euclidean metric used in RLVQ and its variations, a full matrix is more powerful to represent the internal structure of the data appropriately. Large margin generalization bounds can be transferred to this case, leading to bounds that are independent of the input dimensionality. This also holds for local metrics attached to each prototype, which corresponds to piecewise quadratic decision boundaries. The algorithm is tested in comparison to alternative learning vector quantization schemes using an artificial data set, a benchmark multiclass problem from the UCI repository, and a problem from bioinformatics, the recognition of splice sites for C. elegans.