_david-bindel-cornell-university-latent-factor-models-lecture-1.jpg "David Bindel, Cornell University -Latent factor models Lecture 1 from how to make singular matrix Video Screenshot Preview")
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⏲ Duration: 35 min 41 sec
✓ Published: 18-Jun-2019
✓ Published: 18-Jun-2019
Description:
Approximate low-rank factorizations pervade matrix data analysis, often interpreted in terms of latent factor models. After discussing the ubiquitous singular value decomposition (aka PCA), we turn to factorizations such as the interpolative decomposition and the CUR factorization that offer advantages in terms of interpretability and ease of computation. We then discuss constrained approximate factorizations, particularly non-negative matrix factorizations and topic models, which are often part
Approximate low-rank factorizations pervade matrix data analysis, often interpreted in terms of latent factor models. After discussing the ubiquitous singular value decomposition (aka PCA), we turn to factorizations such as the interpolative decomposition and the CUR factorization that offer advantages in terms of interpretability and ease of computation. We then discuss constrained approximate factorizations, particularly non-negative matrix factorizations and topic models, which are often part
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