Kernel generative topographic mapping

نویسندگان

  • Iván Olier
  • Alfredo Vellido
  • Jesús Giraldo
چکیده

A kernel version of Generative Topographic Mapping, a model of the manifold learning family, is defined in this paper. Its ability to adequately model non-i.i.d. data is illustrated in a problem concerning the identification of protein subfamilies from protein sequences.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Appendix: Kernelized Sorting

We compare a number of different object layout algorithms. Figure 1, 2, and 4 depict the results obtained by laying out 320 image objects into a 2D grid with Kernelized Sorting (our method), Self Organizing Map http://www.cis.hut.fi/projects/somtoolbox/, and Generative Topographic Mapping (GTM) http://www.ncrg.aston.ac.uk/GTM/. Figure 3 and 5 show the corresponding cluster members of SOM and GTM.

متن کامل

Mode estimation with topographic maps

The paper reviews thoroughly a variety of issues related to mode estimation. The potential of self-organizing maps as an approach to mode detection is inquired here. The batch version of the standard SOM and a convex adjustment of it are compared with two kernel-based learning rules, namely, the generative topographic mapping and the kernelbased maximum entropy learning rule. A strategy for mod...

متن کامل

Locally Linear Generative Topographic Mapping

We propose a method for non-linear data projection that combines Generative Topographic Mapping and Coordinated PCA. We extend the Generative Topographic Mapping by using more complex nodes in the network: each node provides a linear map between the data space and the latent space. The location of a node in the data space is given by a smooth nonlinear function of its location in the latent spa...

متن کامل

Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model

We introduce GTM-SD (Generative Topographic Mapping for Structured Data), which is the first compositional generative model for topographic mapping of tree-structured data. GTM-SD exploits a scalable bottom-up hidden-tree Markov model that was introduced in Part I of this paper to achieve a recursive topographic mapping of hierarchical information. The proposed model allows efficient exploitati...

متن کامل

Voice Morphing Using the Generative Topographic Mapping

In this paper we address the problem of Voice Morphing. We attempt to transform the spectral characteristics of a source speaker’s speech signal so that the listener would believe that the speech was uttered by a target speaker. The voice morphing system transforms the spectral envelope as represented by a Linear Prediction model. The transformation is achieved by codebook mapping using the Gen...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010