Relative Gradient Learning for Independent Subspace Analysis

Independent subspace analysis (ISA) is a generalization of independent component analysis (ICA), where multidimensional ICA is incorporated with the idea of invariant feature subspaces, allowing components in the same subspace to be dependent, but requiring independence between feature subspaces. In this paper we present a relative gradient algorithm for ISA, derived in the framework of the relative optimization as well as in a direct manner. Empirical comparison with the gradient ISA algorithm, shows that the relative gradient ISA algorithm achieves faster convergence, compared to the conventional gradient algorithm.