Algebraic feature extraction for image recognition based on an optimal discriminant criterion

By Ke Liu, Yong-Qing Cheng, Jing-yu Yang. 1993

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Citations in the corpus

Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “With advances in mathematical tools and algorithms dealing with tensor input, many multilinear discriminant techniques as well as tensor regression have been proposed for image and video classiﬁcation problems such as [18], [19], [20], [21], [22], [23], [24], [25], [26], [27].”
Multilinear Discriminant Analysis for Face Recognition (2007, methodology): “[10] proposed two methods, named 2-D PCA and 2-D LDA1 to conduct PCA and LDA”
Fisherpalms based palmprint recognition (2003, background): “To overcome these problems, another type of features, called algebraic features (Liu et al., 1993), is extracted from palmprints for identity recognition in this paper.”
Discriminant analysis with tensor representation (2005, methodology): “LDA and 2DLDA [5] both optimize the so called Fisher Criterion:”
Two-dimensional PCA: a new approach to appearance-based face representation and recognition (2004, methodology): “Our idea is to project image A , an m (cid:1) n random matrix, onto X by the following linear transformation [15], [19]: Thus, we obtain an m -dimensional projected vector Y , which is called the projected feature vector of image A .”
Multilinear Discriminant Analysis for Higher-Order Tensor Data Classification (2014, result): “We prove both theoretically and experimentally that the value of the scatter ratio criterion in CMDA approaches its extreme value, if it exists, with bounded error, leading to superior and more stable performance in comparison to DATER.”