Information-Geometric Dimensionality Reduction
نویسندگان
چکیده
منابع مشابه
An Information Geometric Framework for Dimensionality Reduction
This report concerns the problem of dimensionality reduction through information geometric methods on statistical manifolds. While there has been considerable work recently presented regarding dimensionality reduction for the purposes of learning tasks such as classification, clustering, and visualization, these methods have focused primarily on Riemannian manifolds in Euclidean space. While su...
متن کاملInformation Preserving Dimensionality Reduction
Dimensionality reduction is a very common preprocessing approach in many machine learning tasks. The goal is to design data representations that on one hand reduce the dimension of the data (therefore allowing faster processing), and on the other hand aim to retain as much task-relevant information as possible. We look at generic dimensionality reduction approaches that do not rely on much task...
متن کاملNonlinear Dimensionality Reduction with Side Information
In this thesis, I look at three problems with important applications in data processing. Incorporating side information, provided by the user or derived from data, is a main theme of each of these problems. This thesis makes a number of contributions. The first is a technique for combining different embedding objectives, which is then exploited to incorporate side information expressed in terms...
متن کاملInformation visualization by dimensionality reduction: a review
Information visualization can be considered a process of transforming similarity relationships between data points to a geometric representation in order to see unseen information. High-dimensionality data sets are one of the main problems of information visualization. Dimensionality Reduction (DR) is therefore a useful strategy to project high-dimensional space onto low-dimensional space, whic...
متن کاملAuxiliary Variational Information Maximization for Dimensionality Reduction
Mutual Information (MI) is a long studied measure of information content, and many attempts to apply it to feature extraction and stochastic coding have been made. However, in general MI is computationally intractable to evaluate, and most previous studies redefine the criterion in forms of approximations. Recently we described properties of a simple lower bound on MI, and discussed its links t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Signal Processing Magazine
سال: 2011
ISSN: 1053-5888
DOI: 10.1109/msp.2010.939536