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User viewing feature can be extracted from TV user's channel viewing data for improving the targeted TV advertising. In this paper, we propose a basic viewing feature extraction algorithm in small sample environment to prove the algorithm logic and check the analysis process quickly. However if the viewing behavior data come from mass TV audience, it requires higher speed feature extraction algorithm...
This paper proposes methods of using restricted Boltzmann machines (RBM) to generate the sequence of lip images for visual speech synthesis. The aim of our proposed methods is to alleviate the over-smoothing effect of the conventional hidden Markov model (HMM) based statistical approach for lip synthesis. Two model structures using RBMs to model and generate lip movements are investigated in this...
Features defined on the cortical surface derived from magnetic resonance imaging provide important information to diagnosis the Alzheimer's disease (AD) and its premonitory symptoms Mild Cognitive Impairment (MCI). In general, the methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise...
The availability of high-resolution (HR) remote sensing multispectral imagery brings opportunities and challenges for land cover classification. The methodology of multiscale segmentation is wildly accepted for feature extraction and classification in HR image. However, the relationship among chosen scale parameters, selected features, and classification accuracy is less considered. A classification...
Dimension reduction (DR) algorithms are generally categorized into feature extraction and feature selection algorithms. In the past, few works have been done to contrast and unify the two algorithm categories. In this work, we introduce a matrix trace oriented optimization framework to provide a unifying view for both feature extraction and selection algorithms. We show that the unified view of DR...
Recently, learning to rank technique has attracted much attention. However, the lack of labeled training data seriously limits its application in real-world tasks. In this paper, we propose to break this bottleneck by considering the cross-domain ldquotransfer of rank learningrdquo problem. Simultaneously, we propose a novel algorithm called TransRank, which can effectively utilize the labeled data...
Dimension reduction for large-scale text data is attracting much attention lately due to the rapid growth of World Wide Web. We can consider dimension reduction algorithms in two categories: feature extraction and feature selection. An important problem remains: it has been difficult to integrate these two algorithm categories into a single framework, making it difficult to reap the benefit of both...
Feature selection is a necessary processing step for class prediction using microarray expression data. Traditional methods select top-ranked genes in terms of their discriminative powers. This strategy unavoidably results in redundancy, whereby correlated features with comparable discriminative powers are equally favorable. Redundancy has many drawbacks among other aspects. As a result, reducing...
In this paper, we propose a method for classifying textures using Genetic Programming (GP). Texture features are extracted from the energy of subimages of the wavelet decomposition. The GP is then used to evolve rules, which are arithmetic combinations of energy features, to identify whether a texture image belongs to certain class. Instead of using only one rule to discriminate the samples, a set...
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