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Grassmannian learning mutual subspace method for image set recognition
The image set is represented by a low-dimensional input subspace; and this input subspace is matched with reference subspaces by a similarity of their canonical angles, an interpretable and easy to compute metric.
Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition
Automatic analysis of bioacoustic signals is a fundamental tool to evaluate the vitality of our planet.
Tensor Analysis with n-Mode Generalized Difference Subspace
In this paper, we present a new method for multi-dimensional data classification that relies on two premises: 1) multi-dimensional data are usually represented by tensors, since this brings benefits from multilinear algebra and established tensor factorization methods; and 2) multilinear data can be described by a subspace of a vector space.
Text Classification based on Word Subspace with Term-Frequency
To measure the similarity between texts, we propose the novel concept of word subspace, which can represent the intrinsic variability of features in a set of word vectors.
