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Unsupervised manifold learning based on multiple feature spaces

机译：基于多个特征空间的无监督流形学习

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摘要

Manifold learning is a well-known dimensionality reduction scheme which can detect intrinsic low-dimensional structures in non-linear high-dimensional data. It has been recently widely employed in data analysis, pattern recognition, and machine learning applications. Isomap is one of the most promising manifold learning algorithms, which extends metric multi-dimensional scaling by using approximate geodesic distance. However, when Isomap is conducted on real-world applications, it may have some difficulties in dealing with noisy data. Although many applications represent a special sample by multiple feature vectors in different spaces, Isomap employs samples in unique observation space. In this paper, two extended versions of Isomap to multiple feature spaces problem, namely fusion of dissimilarities and fusion of geodesic distances, are presented. We have employed the advantages of several spaces and depicted the Euclidean distance on learned manifold that is more compatible to the semantic distance. To show the effectiveness and validity of the proposed method, some experiments have been carried out on the application of shape analysis on MPEG7 CE Part B and Fish data sets.
机译：流形学习是一种众所周知的降维方案，它可以检测非线性高维数据中的固有低维结构。最近，它已广泛用于数据分析，模式识别和机器学习应用程序。 Isomap是最有前途的流形学习算法之一，它通过使用近似测地线距离扩展了度量多维标度。但是，当Isomap在实际应用程序上进行时，在处理嘈杂的数据时可能会有一些困难。尽管许多应用程序通过不同空间中的多个特征向量来表示特殊样​​本，但Isomap在唯一的观察空间中使用了样本。在本文中，提出了Isomap到多个特征空间问题的两个扩展版本，即相异性融合和测地距离融合。我们利用了几个空间的优势，并在学习流形上描述了与语义距离更兼容的欧几里得距离。为了显示所提方法的有效性和有效性，在形状分析在MPEG7 CE B部分和Fish数据集上的应用方面进行了一些实验。

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