Introduction to Statistical Pattern Recognition

By P. Pudil, Petr Somol, M. Haindl. 2006

4,609 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0b2a2ed870d9e947aca41daf751d987ab3163d74

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In Artificial Intelligence

Citations in the corpus

Geometric Mean for Subspace Selection (2009, methodology): “By choosing different mean functions, a series of subspace selection algorithms is obtained, with FLDA included as a special case.”
Face Recognition (2018, positive): “The speedup due to the hierarchical space (represented by a tree) is O(n=log(n)).”
Multi-Instance Dimensionality Reduction (2010, methodology): “Existing dimensionality reduction techniques can be roughly divided into two categories, that is, unsupervised approaches such as PCA (principal component analysis) (Jolliffe 2002), and supervised approaches such as LDA (linear discriminant analysis ) (Fukunaga 1990).”
DCT-Based Iris Recognition (2007, methodology): “…on face recognition [27], [28], where it has been used as a less computationally intensive replacement for the Karhunen-Loeve transform (KLT), which is an optimal technique according to the least squares metric for projecting a large amount of data onto a small dimensional subspace [1], [29].”
Unsteady Flow Sensing and Estimation via the Gappy Proper Orthogonal Decomposition (2004, methodology): “The proper orthogonal decomposition (POD), also known as Karhunen Loéve expansion and principle components analysis, has been widely used for a broad range of applications, including derivation of reduced-order dynamical models [1], image processing [2] and pattern recognition [3].”
Automatic Subspace Clustering of High Dimensional Data (2005, methodology): “Methods such as principal component analysis or Karhunen - Lò eve transformation ( Duda and Hart, 1973; Fukunaga, 1990) optimally transform the original data space into a lower dimensional space by forming dimensions that are linear combinations of given attributes.”
Towards Open World Recognition (2014, background): “NCM classification, in which samples undergo a Mahalanobis transform and then are are associated with a class/cluster mean, is a classic pattern recognition approach [13].”
Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, background): “Ideally, the reduced representations should contain the fewest number of parameters necessary to account for the data’s observed properties [109].”
Model Reduction via Proper Orthogonal Decomposition (2008, background): “in fluid dynamics [4, 14, 32, 35, 36] as well as in signal analysis and pattern recognition [ 9 , 33].”
A survey of multilinear subspace learning for tensor data (2011, methodology): “The criterion used is dependent on the coordinate system, as pointed out in [67], and the tuning parameter is heuristically determined in [23].”
Dimensionality of object representations in monkey inferotemporal cortex (2014, background): “Intrinsic dimensionality is the number of independent parameters required to fully describe a data set (Fukunaga, 1990; Lee & Verleysen, 2007).”
Theory of classification : a survey of some recent advances (2005, background): “A partial list includes Fukunaga [97], Duda and Hart [77], Vapnik and Chervonenkis [233], Devijver and Kittler [70], Vapnik [229] [230], Breiman, Friedman, Olshen, and Stone [53], Natarajan [175], McLachlan [169], Anthony and Biggs [10], Kearns and Vazirani [117], Devroye, Györfi, and Lugosi [72], R”
Multi-Task Learning in Heterogeneous Feature Spaces (2011, methodology): “…of this limitation of existing methods, we propose a new multi-task learning method, called multi-task discriminant analysis (MTDA), which can be seen as a multi-task extension of a widely used supervised dimensionality reduction technique called linear discriminant analysis (LDA) (Fukunaga 1991).”
Nonparametric weighted feature extraction for classification (2004, methodology): “One is that it works well only if the distributions of classes are normal-like distributions [10].”
Vibro-acoustic techniques to diagnose power transformers (2004, methodology): “The proposed diagnostic method is based on vibro-acoustic procedures derived from speech-recognition techniques [1], [2], [5], [6].”
Rotation invariant texture classification using LBP variance (LBPV) with global matching (2010, background): “This can be seen in the Fisher criterion [35]”