Kernel Sliced Inverse Regression: Regularization and Consistency | Zendy

Qiang Wu | Zendy; Feng Liang | Zendy; Sayan Mukherjee | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Kernel Sliced Inverse Regression: Regularization and Consistency

Author(s) -

Qiang Wu,

Feng Liang,

Sayan Mukherjee

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/540725

Subject(s) - mathematics , sliced inverse regression , regularization (linguistics) , inverse , consistency (knowledge bases) , kernel (algebra) , regression , generalization , kernel method , algorithm , inverse problem , mathematical optimization , regularization perspectives on support vector machines , dimension (graph theory) , computer science , artificial intelligence , statistics , support vector machine , tikhonov regularization , discrete mathematics , mathematical analysis , combinatorics , geometry

Kernel sliced inverse regression (KSIR) is a natural framework for nonlinear dimension reduction using the mapping induced by kernels. However, there are numeric, algorithmic, and conceptual subtleties in making the method robust and consistent. We apply two types of regularization in this framework to address computational stability and generalization performance. We also provide an interpretation of the algorithm and prove consistency. The utility of this approach is illustrated on simulated and real data

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research