# Capturing Patterns via Parsimonious t Mixture Models

arXiv: https://arxiv.org/abs/1303.2316 · HTML: https://mlsanity.com/paper/1303.2316/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2013.

## Built on by (most cited first, 2 of 2)

- [Robust factored principal component analysis for matrix-valued outlier accommodation and detection](https://mlsanity.com/paper/2112.06760/) (2021)
- [Parsimonious Mixtures of Skewed Bilinear Factor Analyzers](https://mlsanity.com/paper/2607.14297/) (2026)

## Abstract (arXiv)

This paper exploits a simplified version of the mixture of multivariate t-factor analyzers (MtFA) for robust mixture modelling and clustering of high-dimensional data that frequently contain a number of outliers. Two classes of eight parsimonious t mixture models are introduced and computation of maximum likelihood estimates of parameters is achieved using the alternating expectation conditional maximization (AECM) algorithm. The usefulness of the methodology is illustrated through applications of image compression and compact facial representation.