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Patent is one of the most important carriers of product innovation which provides richer technology information. Patent mining has significant effects for product innovation. Patent information can be act as higher-dimension time-series for it has the characteristics of time and higher-dimension. In this paper, we improved the locally linear embedding algorithm of manifold learning method. Then the...
At present, most of image retrieval applications use Principal Component Analysis (PCA) algorithm to reduce the low-level features of images and rank the similarity of images based on graph structure to enhance the retrieval accuracy with relevance feedback technique. However, there are two issues to consider in traditional image retrieval methods: (1) the feature space of images is probably highly...
By the idea of manifolds learning, this paper presents a new method of dimensionality reduction of high dimensional data. The trait of the method is to exploit image matrixes to directly construct the local scatter matrix and the nonlocal scatter matrix. Its discriminant criterion function is characterized by maximizing the difference between the nonlocal scatter and the local scatter after the samples...
Dimensionality reduction is among the keys in many fields, most of the traditional method can be categorized as local or global ones. In this paper, we consider the dimension reduction problem with prior information is available, namely, semi-supervised dimension reduction. A new dimension reduction method that can explore both the labeled and unlabeled information in the dataset is proposed. The...
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