The Topographic Organization and Visualization of Binary Data Using Multivariate-Bernoulli Latent Variable Models  

Oleh:

Girolami, M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 6 (2001), page 1367-1374.
Topik: bernoulli theorem; topographic organization; visualization; binary data; multivariate bernoulli; latent variable; models

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.6 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A non linear latent variable model for the topographic organization and subsequent visualization of multivariate binary data is presented. The generative topographic mapping (GTM) is a non linear factor analysis model for continuous data which assumes an isotropic Gaussian noise model and performs uniform sampling from a two - dimensional (2 - D) latent space. Despite the, success of the GTM when applied to continuous data the development of a similar model for discrete binary data has been hindered due, in part, to the nonlinear link function inherent in the binomial distribution which yields a log - likelihood that is non linear in the model parameters. The paper presents an effective method for the parameter estimation of a binary latent variable model - a binary version of the GTM - by adopting a variational approximation to the binomial likelihood. This approximation thus provides a log-likelihood which is quadratic in the model parameters and so obviates the necessity of an iterative M - step in the expectation maximization (EM) algorithm. The power of this method is demonstrated on two significant application domains, handwritten digit recognition and the topographic organization of semantically similar text - based documents.

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