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Difference between PCA and t-SNE

PCA is one of the most important methods of dimensionality reduction for visualizing data. t-SNE is also an unsupervised non-linear dimensionality reduction and data visualization technique
Difference between PCA and t-SNE

Principal Component analysis (PCA)

  1. It is a linear Dimensionality reduction technique.
  2. It tries to preserve the global structure of the data.
  3. It does not work well as compared to t-SNE.
  4. It does not involve Hyperparameters.
  5. It gets highly affected by outliers.
  6. PCA is a deterministic algorithm
  7. It works by rotating the vectors for preserving variance.
  8. We can find decide on how much variance to preserve using eigen values.

t-distributed stochastic neighbourhood embedding (t-SNE)

  1. It is a non-linear Dimensionality reduction technique.
  2. It tries to preserve the local structure(cluster) of data.
  3. It is one of the best dimensionality reduction technique.
  4. It involves Hyperparameters such as perplexity, learning rate and number of steps.
  5. It can handle outliers
  6. It is a non-deterministic or randomised algorithm.
  7. It works by minimising the distance between the point in a guassian.
  8. We cannot preserve variance instead we can preserve distance using hyperparameters.
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