singular value decomposition
[/ˈsɪŋɡjələr ˈvæljuː ˌdɛkəmˈpoʊzɪʃən/]
nounpl: singular value decompositions
decomposição em valores singulares
1. A matrix factorization technique in linear algebra that decomposes a matrix into three component matrices: U, Σ (sigma), and V*, where U and V are unitary matrices and Σ is a diagonal matrix containing singular values
The singular value decomposition is widely used in data compression and noise reduction algorithms.
A decomposição em valores singulares é amplamente utilizada em algoritmos de compressão de dados e redução de ruído.
2. A mathematical method for finding the optimal low-rank approximation of a matrix
Using singular value decomposition, we can identify the most important features in a dataset.
Usando decomposição em valores singulares, podemos identificar as características mais importantes em um conjunto de dados.
3. A fundamental tool in machine learning for dimensionality reduction and feature extraction
The algorithm employs singular value decomposition to reduce the dimensionality of high-dimensional feature spaces.
O algoritmo emprega decomposição em valores singulares para reduzir a dimensionalidade de espaços de características de alta dimensão.
This is a highly technical term primarily used in academic and professional contexts within mathematics, physics, computer science, and data science. It originated from mathematical research and is universally recognized by its acronym 'SVD' in both English and Portuguese-speaking technical communities. The term is essential in modern machine learning and artificial intelligence applications.
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