Presentation Type
Event
Student
No
Track
Tools
Abstract
Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal Component Analysis (MPCA) and Mulit-Linear Discriminant Analysis (MLDA).
Start Date
2-5-2019 1:00 PM
End Date
2-5-2019 1:50 PM
Included in
Applied Statistics Commons, Artificial Intelligence and Robotics Commons, Categorical Data Analysis Commons, Statistical Methodology Commons
Session: 4 Multilinear Subspace Learning and its Applications to Machine Learning
Pasque 255
Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal Component Analysis (MPCA) and Mulit-Linear Discriminant Analysis (MLDA).