Zero sample classification method based on multi-mode dictionary learning

A technology of dictionary learning and classification methods, applied in the field of zero-sample classification, can solve problems such as the inability to fully represent the semantics of categories, achieve simple and efficient practicality, and improve training efficiency
CN106485271AActive Publication Date: 2017-03-08TIANJIN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
TIANJIN UNIV
Publication Date
2017-03-08

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Abstract

The invention provides a zero sample classification method based on multi-mode dictionary learning. The method comprises steps of: establishing a multi-mode dictionary learning model; using the multi-mode dictionary learning model to learn a dictionary matrix D and a compatible matrix V; and by use of the learned dictionary matrix D and the compatible matrix V, achieving zero sample classification. According to the invention, a training sample is used for leaning a dictionary matrix with shared category; the sample is embedded into a hidden space by spanning dictionary atoms; by use of the sample, vectors are embedded into the hidden space; and based on the corresponding relation between a category semantic vector corresponding to the sample and the category, a combined embedding model is learned.
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