Probabilistic PCA for samples with heterogeneous quality
Author dahong67
1 Star
Updated Last
1 Year Ago
Started In
April 2020

HePPCAT: HEteroscedastic Probabilistic PCA Technique

Project Status: Active – The project has reached a stable, usable state and is being actively developed. Build Status Coverage

👋 This package provides research code and work is ongoing. If you are interested in using it in your own research, I'd love to hear from you and collaborate! Feel free to write: dahong67@wharton.upenn.edu

Please cite the following paper for this technique:

David Hong, Kyle Gilman, Laura Balzano, Jeffrey A. Fessler. "HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise", IEEE Transactions on Signal Processing 69:4819-4834, Aug. 2021. https://doi.org/10.1109/TSP.2021.3104979 https://arxiv.org/abs/2101.03468.

In BibTeX form:

  title   = "{HePPCAT}: Probabilistic {PCA} for Data with Heteroscedastic Noise",
  author  = "David Hong and Kyle Gilman and Laura Balzano and Jeffrey A. Fessler",
  journal = "{IEEE} Transactions on Signal Processing",
  year    = "2021",
  volume  = "69",
  pages   = "4819--4834",
  DOI     = "10.1109/tsp.2021.3104979",

What is Heteroscedastic Probabilistic PCA (HePPCAT)?

HePPCAT is a probabilistic Principal Component Analysis (PCA) technique for data that has samples with heterogeneous quality, i.e., noise that is heteroscedastic across samples.

Illustration: data points with a group of noisier samples (blue points) and a group of cleaner samples (green points).

2D illustration

Homoscedastic PPCA estimates only one noise variance for the whole data, and treats samples as though they were all equally noisy. Recovery of latent components can degrade a lot due to the noisier samples, even though the rest of the samples are relatively clean.

HePPCAT estimates latent components along with separate noise variances for each group. It accounts for heterogeneous quality among the samples and is generally more robust.

It's not just a "cool cat"!

Quick start: how to use HePPCAT

Installation: this package is registered and can be installed via the package manager.

pkg> add HePPCAT

Main function: the main function is called heppcat. Type ?heppcat for the docstring.

help?> heppcat
search: heppcat HePPCAT HePPCATModel


  Estimate probabilistic PCA model for noise that is heteroscedastic across samples.

  Required Inputs

    •  Y : list of matrices (each column is a sample)

    •  k : number of factors

    •  iters : number of iterations to run

  Optional Keyword Arguments

    •  init : initial model (will be modified in-place)

    •  vknown : variances are known (do not update) default false

    •  varfloor : lower bound for variance iterates (useful if the iterates are degenerating to zero) default = 0

  Output is a HePPCATModel object.

It returns a HePPCATModel:

help?> HePPCATModel
search: HePPCATModel


  Model parameters for probabilistic PCA with noise that is heteroscedastic across samples.
  This is the return type of heppcat(_), the corresponding estimation function.


    •  F :: Matrix factor matrix (computed via F = U*sqrt(Diagonal(λ))*Vt)

    •  U :: Matrix{S} eigenvectors of factor covariance F*F'

    •  λ :: Vector{T} eigenvalues of factor covariance F*F' (spike eigenvalues)

    •  Vt :: Matrix{S} (transposed) eigenvectors of F'*F (i.e., right singular vectors of F)

    •  v :: Vector{T} noise variances

See the demo/ directory and the paper code repo for some examples!

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