A Novel Random Forest Dissimilarity Measure for Multi-View Learning

07/06/2020
by   Hongliu Cao, et al.
0

Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. This is a challenge that can be met nowadays if there is a large amount of data available for learning. However, this is not necessarily true for all real-world problems, where data are sometimes scarce (e.g. problems related to the medical environment). In these situations, an effective strategy is to use intermediate representations based on the dissimilarities between instances. This work presents new ways of constructing these dissimilarity representations, learning them from data with Random Forest classifiers. More precisely, two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements including the original RF Proximity measurement and the Large Margin Nearest Neighbor (LMNN) metric learning measurement.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/16/2020

Random Forest for Dissimilarity-based Multi-view Learning

Many classification problems are naturally multi-view in the sense their...
research
06/20/2018

Dynamic voting in multi-view learning for radiomics applications

Cancer diagnosis and treatment often require a personalized analysis for...
research
03/21/2018

Multi-view Metric Learning in Vector-valued Kernel Spaces

We consider the problem of metric learning for multi-view data and prese...
research
05/25/2014

Multi-view Metric Learning for Multi-view Video Summarization

Traditional methods on video summarization are designed to generate summ...
research
04/13/2023

Deep Metric Multi-View Hashing for Multimedia Retrieval

Learning the hash representation of multi-view heterogeneous data is an ...
research
12/13/2021

Incorporating Measurement Error in Astronomical Object Classification

Most general-purpose classification methods, such as support-vector mach...
research
02/23/2021

Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning

In 2001, Leo Breiman wrote of a divide between "data modeling" and "algo...

Please sign up or login with your details

Forgot password? Click here to reset