Feature structure fusion and its application

Abstract The structure of data is important to the recognition of data. It is a fundamental question how measures and complements the structure of multi-features, because the fusion structure of multi-features is more complete than that of the single feature. To settle the question, we propose three methods for feature structure fusion in feature vectors or feature vector spaces. Firstly, the applicability of the different metric is analyzed. Secondly, optimization questions of various features are constructed based on manifold learning methods. Finally, multiple target optimization questions are transformed to a single target optimization question, and the principle of feature structure fusion is uncovered. In the classification of shape analysis and human action recognition, it is proven that structure fusion methods are effective.

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