Designing various component analysis at will
This work offers a flexible framework for researchers and practitioners in machine learning to design custom component analysis methods, though it appears incremental as it builds upon existing techniques.
The paper tackles the problem of designing component analysis methods by proposing a generic framework using Generalized Pairwise Expression (GPE) for scatter and Gram matrices, which includes standard methods, regularization, weighted extensions, clustering, and semi-supervised extensions, and provides a simple methodology for creating new methods by combining GPE templates.
This paper provides a generic framework of component analysis (CA) methods introducing a new expression for scatter matrices and Gram matrices, called Generalized Pairwise Expression (GPE). This expression is quite compact but highly powerful: The framework includes not only (1) the standard CA methods but also (2) several regularization techniques, (3) weighted extensions, (4) some clustering methods, and (5) their semi-supervised extensions. This paper also presents quite a simple methodology for designing a desired CA method from the proposed framework: Adopting the known GPEs as templates, and generating a new method by combining these templates appropriately.