The subspace-based methods are effectively applied to classify sets of feature vectors by modeling them as subspaces. It is, however, difficult to appropriately determine the subspace dimensionality in advance for better performance. For alleviating such issue, we present a generalized mutual subspace method by introducing soft weighting across the basis vectors of the subspace. The bases are effectively combined via the soft weights to measure the subspace similarities (angles) without definitely setting the subspace dimensionality. By using the soft weighting, we consequently propose a novel mutual subspace-based method to construct the discriminative space which renders more discriminative subspace similarities. In the experiments on 3D object recognition using image sets, the proposed methods exhibit stably favorable performances compared to the other subspace-based methods.
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