Title:
Robust Generative Subspace Modeling: The Subspace t Distribution
Robust Generative Subspace Modeling: The Subspace t Distribution
| dc.contributor.author | Khan, Zia | |
| dc.contributor.author | Dellaert, Frank | |
| dc.date.accessioned | 2004-07-27T20:22:51Z | |
| dc.date.available | 2004-07-27T20:22:51Z | |
| dc.date.issued | 2004 | |
| dc.description.abstract | Linear latent variable models such as statistical factor analysis (SFA) and probabilistic principal component analysis (PPCA) assume that the data are distributed according to a multivariate Gaussian. A drawback of this assumption is that parameter learning in these models is sensitive to outliers in the training data. Approaches that rely on M-estimation have been introduced to render principal component analysis (PCA) more robust to outliers. M-estimation approaches assume the data are distributed according to a density with heavier tails than a Gaussian. Yet, these methods are limited in that they fail to define a probability model for the data. Data cannot be generated from these models, and the normalized probability of new data cannot evaluated. To address these limitations, we describe a generative probability model that accounts for outliers. The model is a linear latent variable model in which the marginal density over the data is a multivariate t, a distribution with heavier tails than a Gaussian. We present a computationally efficient expectation maximization (EM) algorithm for estimating the model parameters, and compare our approach with that of PPCA on both synthetic and real data sets. | en |
| dc.format.extent | 278840 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/60 | |
| dc.language.iso | en | |
| dc.publisher | Georgia Institute of Technology | en |
| dc.relation.ispartofseries | GVU Technical Report;GIT-GVU-04-11 | |
| dc.subject | Principal component analysis | en |
| dc.subject | Factor analysis | en |
| dc.subject | t distribution | en |
| dc.subject | Latent variable model | en |
| dc.subject | Outliers | en |
| dc.subject | Robust | en |
| dc.title | Robust Generative Subspace Modeling: The Subspace t Distribution | en |
| dc.type | Text | |
| dc.type.genre | Technical Report | |
| dspace.entity.type | Publication | |
| local.contributor.author | Dellaert, Frank | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| local.contributor.corporatename | GVU Center | |
| local.relation.ispartofseries | GVU Technical Report Series | |
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