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Local features have been widely used in computer vision tasks, e.g., human action recognition, but it tends to be an extremely challenging task to deal with large-scale local features of high dimensionality with redundant information. In this paper, we propose a novel fully supervised local descriptor learning algorithm called discriminative embedding method based on the image-to-class distance (I2CDDE)...
The word-level sentiment analysis is an essential issue in opinion mining. One challenge in this field is that not so many lexical items as expected have been labeled with sentimental opinions, especially in Chinese. There are two ways of rating words: one is manual marking which costs lots of resources, energy and time; the other is machine marking which is efficient, convenient and time-saving....
This paper presents a novel palmprint recognition approach based on generalized discriminant analysis (GDA). The method uses GDA to extract palmprint features for palmprint recognition. GDA project palmprint images from a high-dimensional input space to a lower dimensional feature space, in which the palmprints from the different palms can be discriminated much more efficiently. The experiment results...
A forecast learning method of kernel principal component analysis (KPCA) is presented for specific emitter identification (SEI) application. By constructing a symmetrical decomposition of the kernel matrix, we derived a new algorithm of incremental KPCA. Based on it, the forecast capability is developed by creating dummy samples whose kernel vectors are an extrapolation of the kernel matrix. The advance...
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