34,715 research outputs found
Optimal Clustering Framework for Hyperspectral Band Selection
Band selection, by choosing a set of representative bands in hyperspectral
image (HSI), is an effective method to reduce the redundant information without
compromising the original contents. Recently, various unsupervised band
selection methods have been proposed, but most of them are based on
approximation algorithms which can only obtain suboptimal solutions toward a
specific objective function. This paper focuses on clustering-based band
selection, and proposes a new framework to solve the above dilemma, claiming
the following contributions: 1) An optimal clustering framework (OCF), which
can obtain the optimal clustering result for a particular form of objective
function under a reasonable constraint. 2) A rank on clusters strategy (RCS),
which provides an effective criterion to select bands on existing clustering
structure. 3) An automatic method to determine the number of the required
bands, which can better evaluate the distinctive information produced by
certain number of bands. In experiments, the proposed algorithm is compared to
some state-of-the-art competitors. According to the experimental results, the
proposed algorithm is robust and significantly outperform the other methods on
various data sets
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
In multi-objective decision planning and learning, much attention is paid to
producing optimal solution sets that contain an optimal policy for every
possible user preference profile. We argue that the step that follows, i.e,
determining which policy to execute by maximising the user's intrinsic utility
function over this (possibly infinite) set, is under-studied. This paper aims
to fill this gap. We build on previous work on Gaussian processes and pairwise
comparisons for preference modelling, extend it to the multi-objective decision
support scenario, and propose new ordered preference elicitation strategies
based on ranking and clustering. Our main contribution is an in-depth
evaluation of these strategies using computer and human-based experiments. We
show that our proposed elicitation strategies outperform the currently used
pairwise methods, and found that users prefer ranking most. Our experiments
further show that utilising monotonicity information in GPs by using a linear
prior mean at the start and virtual comparisons to the nadir and ideal points,
increases performance. We demonstrate our decision support framework in a
real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at
https://github.com/lmzintgraf/gp_pref_elici
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
People are rated and ranked, towards algorithmic decision making in an
increasing number of applications, typically based on machine learning.
Research on how to incorporate fairness into such tasks has prevalently pursued
the paradigm of group fairness: giving adequate success rates to specifically
protected groups. In contrast, the alternative paradigm of individual fairness
has received relatively little attention, and this paper advances this less
explored direction. The paper introduces a method for probabilistically mapping
user records into a low-rank representation that reconciles individual fairness
and the utility of classifiers and rankings in downstream applications. Our
notion of individual fairness requires that users who are similar in all
task-relevant attributes such as job qualification, and disregarding all
potentially discriminating attributes such as gender, should have similar
outcomes. We demonstrate the versatility of our method by applying it to
classification and learning-to-rank tasks on a variety of real-world datasets.
Our experiments show substantial improvements over the best prior work for this
setting.Comment: Accepted at ICDE 2019. Please cite the ICDE 2019 proceedings versio
- …