YouTipPytorch Torch Pca_Lowrank
# PyTorch torch.pca_lowrank Function
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[ of a low-rank matrix. This function uses randomized algorithms to efficiently compute PCA, making it suitable for large-scale datasets.
### Function Definition
torch.pca_lowrank(A, q, center, niter)
### Parameter Description
* `A`: Input matrix
* `q`: Number of power iterations (optional, default 6)
* `center`: Whether to center the data (optional, default True)
* `niter`: Number of random iterations (optional, default 2)
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## Usage Example
## Example
import torch
# Create a data matrix
A = torch.randn(100,10)
# Compute low-rank PCA
U, S, V = torch.pca_lowrank(A)
print("Principal compone...
```