1 / 10

Presented by: John Paisley Duke University, ECE

The Phylogenetic Indian Buffet Process : A Non-Exchangeable Nonparametric Prior for Latent Features By: Kurt T. Miller, Thomas L. Griffiths and Michael I. Jordan ICML 2008. Presented by: John Paisley Duke University, ECE. Motivation.

may
Download Presentation

Presented by: John Paisley Duke University, ECE

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent FeaturesBy: Kurt T. Miller, Thomas L. Griffiths and Michael I. JordanICML 2008 Presented by: John Paisley Duke University, ECE

  2. Motivation • Nonparametric models are often used with the assumption of exchangeability. • The Indian Buffet Process is an example • Sometimes, non-exchangeable models might be more appropriate. • The Phylogenetic Indian Buffet Process • Similar to the IBF, but uses additional information of how related diners are with each other. • These relationships are captured in a tree structure.

  3. Indian Buffet Process

  4. Phylogenetic Indian Buffet Process • Uses a tree to model columns zk • This is done as follows: • Assign the root node to be zero • Along an edge of distance t, let this change to a 1 with probability , where . The distance from every leaf to the root is 1. • If a 0 is changed to a 1 along a path to a node, all subsequent nodes are 1 and therefore so are the leaves.

  5. Sampling Issues • For (1), use the sum-product algorithm (Pearl, 1988). • For (2), use the chain rule of probability. • An MCMC inference algorithm is given in detail.

  6. Experimental Results • Elimination by Aspects (EBA) model • A Choice Model • Let there be i objects and zik indicate the ith object has the kth feature. Let each feature have a weight, wk. The EBA model defines the probability of choosing object I over j as • The likelihood of an observation matrix, X, is • This has been modeled using the IBP.

  7. Experimental Results • Consider now an underlying tree structure to this model. • Preference trees: Out of 9 personalities, 3 movie stars, 3 athletes and 3 politicians, people made the 36 pairwise choices of whom they would rather spend time with. Here, L is the length of the edge of each general category to a leaf. • A soft version of this tree is modeled with the pIBP using data generated from this model with L = 0.1

  8. Experimental Results • Example results: As the number of samples decreases, the pIBP is able to infer the structure better than the IBP because of the prior.

  9. Experimental Results • As can be seen, the additional structure in the model produces better results.

More Related