Relevance learning in generative topographic maps
نویسندگان
چکیده
The generative topographic map (GTM) provides a flexible statistical model for unsupervised data inspection and topographic mapping. However, it shares the property of most unsupervised tools that noise in the data cannot be recognized as such and, in consequence, is visualized in the map. The framework of relevance learning or learning metrics as introduced in [4, 6] offers an elegant way to shape the metric according to auxiliary information at hand such that only those aspects are displayed in distance-based approaches which are relevant for a given classification task. Here we introduce the concept of relevance learning into GTM such that the metric is shaped according to auxiliary class labels. Relying on the prototype-based nature of GTM, several efficient realizations of this paradigm are developed and compared on a couple of benchmarks.
منابع مشابه
Mode estimation with topographic maps
The paper reviews thoroughly a variety of issues related to mode estimation. The potential of self-organizing maps as an approach to mode detection is inquired here. The batch version of the standard SOM and a convex adjustment of it are compared with two kernel-based learning rules, namely, the generative topographic mapping and the kernelbased maximum entropy learning rule. A strategy for mod...
متن کاملRelevance learning for time series inspection
By means of local neighborhood regression and time windows, the generative topographic mapping (GTM) allows to predict and visually inspect time series data. GTM itself, however, is fully unsupervised. In this contribution, we propose an extension of relevance learning to time series regression with GTM. This way, the metric automatically adapts according to the relevant time lags resulting in ...
متن کاملPreliminary theoretical results on a feature relevance determination method for Generative Topographic Mapping
Feature selection (FS) has long been studied in classification and regression problems, following diverse approaches and resulting on a wide variety of methods, usually grouped as either filters or wrappers. In comparison, FS for unsupervised learning has received far less attention. For many real problems concerning unsupervised multivariate data clustering, FS becomes an issue of paramount im...
متن کاملFrom horizontal to vertical collaborative clustering using generative topographic maps
Collaborative clustering is a recent field of Machine Learning that shows similarities with both ensemble learning and transfer learning. Using a two-step approach where different clustering algorithms first process data individually and then exchange their information and results with a goal of mutual improvement, collaborative clustering has shown promising performances when trying to have se...
متن کاملMaking nonlinear manifold learning models interpretable: The manifold grand tour
Dimensionality reduction is required to produce visualizations of high dimensional data. In this framework, one of the most straightforward approaches to visualising high dimensional data is based on reducing complexity and applying linear projections while tumbling the projection axes in a defined sequence which generates a Grand Tour of the data. We propose using smooth nonlinear topographic ...
متن کامل