Aug 2, 2023 · Our CosMPCA method considers the relationship between the reconstruction error and projection scatter and selects the cosine metric. In addition ...
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Abstract— Existing two-dimensional principal component analysis methods can only handle second-order tensors (i.e., matrices). However, with the advancement ...
We extend the robust Angle 2DPCA method to a multilinear method and propose Cosine Multilinear Principal Component Analysis (CosMPCA) for tensor representation.
In particular, Principal Component Analysis (PCA) is a multivariate statistical technique (see Note 1) applied to systematically reduce the number of dimensions ...
Aug 18, 2020 · Principal component analysis, or PCA, is a statistical procedure that allows you to summarize the information content in large data tables.
Nov 15, 2007 · This paper proposes a cosine similarity ensemble (CSE) method for learning similarity. In CSE, diversity is guaranteed by using multiple cosine ...
Feb 13, 2023 · In this tutorial, you'll learn how to use R PCA (Principal Component Analysis) to extract data with many variables and create visualizations to display that ...
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Abstract—This paper introduces a multilinear principal com- ponent analysis (MPCA) framework for tensor object feature extraction.
Missing: Cosine | Show results with:Cosine
Multilinear principal component analysis (MPCA) is a multilinear extension of principal component analysis (PCA) that is used to analyze M-way arrays.
Missing: Cosine | Show results with:Cosine
This paper presents a new approach to multilinear two dimen- sional and two directional principal component analysis. ... resentation and recognition,” ...
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