HI,

I want to apply PCA on model outputs. The code is:

```
def PCA_svd(x, k, center=True):
n = x.size()[0]
ones = torch.ones(n).view([n,1])
h = ((1/n) * torch.mm(ones, ones.t()))# if center else torch.zeros(n*n).view([n,n])
H = torch.eye(n) - h
#H = H.cuda()
X_center = torch.mm(H.double().cuda(), x.double().cuda())
u, s, v = torch.svd(X_center)
components = v[:k].t()
#explained_variance = torch.mul(s[:k], s[:k])/(n-1)
return components
```

The model output is composed of feature vectors of shape 512. So, we can assume the model output = torch.rand(50,512) where 50 is the batchsize.

Next I am using the function on these model outputs as:

```
feature = PCA_svd(model_output,128)
```

But, I am getting this error when I run the codes for a dataset of about 5000 images.

```
RuntimeError: svd_cuda: For batch 0: U(51,51) is zero, singular U.
```

Funny thing is that when I run it for a smaller dataset of lets say 3000 images, I don’t face this error.

I don’t understand it. Is it a bug? Or, is there something wrong in my PCA_svd function.