Functional k k -means inverse regression
A new dimension reduction method is proposed for functional multivariate regression with a multivariate response and a functional predictor by extending the functional sliced inverse regression model. Naive application of existing dimension reduction techniques for univariate response will create too many hyper-rectangular slices. To avoid this curse of dimensionality, a new slicing method is proposed by clustering over the space of the multivariate response, which generates a much smaller set of slices of flexible shapes. The proposed method can be applied to any number of response variables and can be particularly useful for exploratory analysis. In addition, a new eigenvalue-based method for determining the dimensionality of the reduced space is developed. Real and simulation data examples are then presented to demonstrate the effectiveness of the proposed method.
dimension reduction multivariate regression effective direction reduction functional data k k -means clustering k k k k k