Principle Component Analysis

By David Dapeng Zhang, Xiaoyuan Jing, Jian Yang. 2006

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Citations in the corpus

Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics (2016, methodology): “Interestingly, as we move to the tail of the PCA projection space, the picture start to change signiﬁcantly.”
From Data to Optimal Decision Making: A Data-Driven, Probabilistic Machine Learning Approach to Decision Support for Patients With Sepsis (2015, unknown): “Principal Component Analysis (PCA) was also used to assess whether linear transformation of the feature space and dimensionality reduction can be achieved in this case [38].”
A codebook-free and annotation-free approach for fine-grained image categorization (2012, methodology): “Therefore we use principle component analysis (PCA) [16] for feature dimension reduction.”
Constrained Nonnegative Matrix Factorization for Image Representation (2012, methodology): “PCA [9] and SVD [10] decompose the matrix as the linear combination of principle components.”
Topological Data Analysis (2012, background): “For example, a disc is contractible, but not homeomorphic, to a point.”
Cognitive Development of Fluent Word Reading Does Not Qualitatively Differ Between Transparent and Opaque Orthographies (2010, methodology): “We initially extracted six factors (using Jolliffe’s criterion, which allows eigenvalues of .70 and higher; Jolliffe, 1972, 1986): a Rapid Naming factor, a Letter–Speech Sound Accuracy factor, a Verbal Working Memory factor, a Letter– Speech Sound Speed factor, and a Phonological Awareness factor.”
Spectral imaging: Principles and applications (2006, methodology): “It uses statistical analysis of the whole data set (all the spectra in the image) and finds the similarities and differences in the data (37).”