DeepAI AI Chat
Log In Sign Up

Label Propagation for Learning with Label Proportions

10/24/2018
by   Rafael Poyiadzi, et al.
0

Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual labels is expensive and label aggregates are more easily obtained. In the healthcare domain, it is a burden for a patient to keep a detailed diary of their daily routines, but often they will be amenable to provide higher level summaries of daily behavior. We present a novel and efficient graph-based algorithm that encourages local smoothness and exploits the global structure of the data, while preserving the `mass' of each bag.

READ FULL TEXT

page 1

page 2

page 3

page 4

02/24/2014

On Learning from Label Proportions

Learning from Label Proportions (LLP) is a learning setting, where the t...
03/04/2022

Learning from Label Proportions by Learning with Label Noise

Learning from label proportions (LLP) is a weakly supervised classificat...
12/22/2014

On Learning Vector Representations in Hierarchical Label Spaces

An important problem in multi-label classification is to capture label p...
07/29/2016

Semi-supervised evidential label propagation algorithm for graph data

In the task of community detection, there often exists some useful prior...
03/13/2023

Label Information Bottleneck for Label Enhancement

In this work, we focus on the challenging problem of Label Enhancement (...
11/20/2019

Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for Classification

The graph-based semi-supervised label propagation algorithm has delivere...
06/30/2016

Ballpark Learning: Estimating Labels from Rough Group Comparisons

We are interested in estimating individual labels given only coarse, agg...