Robust PCA and clustering in noisy mixtures

  title={Robust PCA and clustering in noisy mixtures},
  author={S. Charles Brubaker},
This paper presents a polynomial algorithm for learning mixtures of logconcave distributions in R in the presence of malicious noise. That is, each sample is corrupted with some small probability, being replaced by a point about which we can make no assumptions. A key element of the algorithm is Robust Principle Components Analysis (PCA), which is less susceptible to corruption by noisy points. While noise may cause standard PCA to collapse well-separated mixture components so that they are… CONTINUE READING
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