Unsupervised Data Classification for Convex and Non Convex Classes

Non convex technologies and economic efficiency measure with imprecise data

Unsupervised Classification via Convex Absolute Value Inequalities

We consider the problem of classifying completely unlabeled data by using convex inequalities that contain absolute values of the data. This allows each data point to belong to either one of two classes by entering the inequality with a plus or minus value. By using such absolute value inequalities (AVIs) in support vector machine classifiers, unlabeled data can be successfully partitioned into...

Convex NMF on Non-Convex Massiv Data

We present an extension of convex-hull nonnegative matrix factorization (CH-NMF) which was recently proposed as a large scale variant of convex non-negative matrix factorization (CNMF) or Archetypal Analysis (AA). CH-NMF factorizes a non-negative data matrix V into two non-negative matrix factors V ≈ WH such that the columns of W are convex combinations of certain data points so that they are r...

non convex technologies and economic efficiency measure with imprecise data

A Non-generative Framework and Convex Relaxations for Unsupervised Learning

We will describe a novel theoretical framework for unsupervised learning which is not based on generative assumptions. It is comparative, and allows to avoid known computational hardness results and improper algorithms based on convex relaxations. We show how several families of unsupervised learning models, which were previously only analyzed under probabilistic assumptions and are otherwise p...