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Multi-dataset Low-rank Matrix Factorization
Hossein Valavi
, Peter Jeffrey Ramadge
Electrical and Computer Engineering
Princeton Institute for Computational Science and Engineering
Center for Statistics & Machine Learning
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Dive into the research topics of 'Multi-dataset Low-rank Matrix Factorization'. Together they form a unique fingerprint.
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Keyphrases
Hyperalignment
100%
Shared Response Model
100%
Low-rank Matrix Factorization
100%
Multi-dataset
100%
Functional Magnetic Resonance Imaging
33%
Solution Set
33%
Gradient Descent
33%
Number of Features
33%
Rotation Matrix
33%
Low-dimensional Representation
33%
Matrix Factorization
33%
Structures in Data
33%
Training Data
33%
Similarity Matrix
33%
Multi-subject
33%
Overfitting
33%
Google TensorFlow
33%
Desired Conditions
33%
General Rotation
33%
Dataset Analysis
33%
Matrix Factorization Method
33%
Project Space
33%
Mathematics
Matrix (Mathematics)
100%
Principal Component Analysis
100%
Response Model
60%
Necessary and Sufficient Condition
20%
Solution Set
20%
Training Data
20%
Rotation Matrix
20%
Factorization Method
20%
Computer Science
Matrix Factorization
100%
Sufficient Condition
25%
Gradient Descent
25%
Training Data
25%
Rotation Matrix
25%
Dimensional Matrix
25%
TensorFlow
25%