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Preserving sample's pair wise similarity is essential for feature selection. In supervised learning, labels can be used as a direct measure to check whether two samples are similar with each other. In unsupervised learning, however, such similarity information is usually unavailable. In this paper, we propose a new feature selection method through spectral clustering based on discriminative information...
In this paper, we proposed an advanced face analysis platform for large-scale consumer photos, namely PFAP. Leveraging Client/Server architecture, the platform provides users high-performance face clustering and near-real time image retrieval service. Advanced face analysis schema, two-level parallel computing architecture and analysis as a service are three key innovations in PFAP. In face analysis...
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