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  • Trace ratio optimization with feature correlation mining for multiclass discriminant analysis

    Author(s)
    Rezaei Boroujeni, Forough
    Wang, S
    Li, Z
    West, Nicholas
    Stantic, B
    Yao, L
    Long, G
    Griffith University Author(s)
    Stantic, Bela
    West, Nic P.
    Year published
    2018
    Metadata
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    Abstract
    Fisher's linear discriminant analysis is a widely accepted dimensionality reduction method, which aims to find a transformation matrix to convert feature space to a smaller space by maximising the between-class scatter matrix while minimising the within-class scatter matrix. Although the fast and easy process of finding the transformation matrix has made this method attractive, overemphasizing the large class distances makes the criterion of this method suboptimal. In this case, the close class pairs tend to overlap in the subspace. Despite different weighting methods having been developed to overcome this problem, there is ...
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    Fisher's linear discriminant analysis is a widely accepted dimensionality reduction method, which aims to find a transformation matrix to convert feature space to a smaller space by maximising the between-class scatter matrix while minimising the within-class scatter matrix. Although the fast and easy process of finding the transformation matrix has made this method attractive, overemphasizing the large class distances makes the criterion of this method suboptimal. In this case, the close class pairs tend to overlap in the subspace. Despite different weighting methods having been developed to overcome this problem, there is still a room to improve this issue. In this work, we study a weighted trace ratio by maximising the harmonic mean of the multiple objective reciprocals. To further improve the performance, we enforce the 2,1-norm to the developed objective function. Additionally, we propose an iterative algorithm to optimise this objective function. The proposed method avoids the domination problem of the largest objective, and guarantees that no objectives will be too small. This method can be more beneficial if the number of classes is large. The extensive experiments on different datasets show the effectiveness of our proposed method when compared with four state-of-the-art methods.
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    Conference Title
    32nd AAAI Conference on Artificial Intelligence, AAAI 2018
    Publisher URI
    https://aaai.org/Conferences/conferences.php
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/384466
    Collection
    • Conference outputs

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