378 research outputs found
Simple Geometric Approach of Identification and Control Using Floating Basis Vectors for Representation
As a plausible alternative of certain sophisticated soft computing approaches trying to identify complete and static system models, a simple adaptive controller is outlined that creates only a temporal model. This model can be built up and maintained step-by-step on the basis of slowly fading information by the use of simple updating rules consisting of finite algebraic steps of lucid geometric interpretation. The method may be used for filling in the lookup tables or rule bases of the above representations
experimentally. The method is tested by the use of a simple dynamic system as a typical paradigm via simulation.N/
Multi-facet determination for clustering with Bayesian networks
Real world applications of sectors like industry, healthcare or finance usually generate data of
high complexity that can be interpreted from different viewpoints. When clustering this type of
data, a single set of clusters may not suffice, hence the necessity of methods that generate multiple
clusterings that represent different perspectives. In this paper, we present a novel multi-partition
clustering method that returns several interesting and non-redundant solutions, where each of them
is a data partition with an associated facet of data. Each of these facets represents a subset of the
original attributes that is selected using our information-theoretic criterion UMRMR. Our approach
is based on an optimization procedure that takes advantage of the Bayesian network factorization
to provide high quality solutions in a fraction of the time
Natural Graph Wavelet Packet Dictionaries
We introduce a set of novel multiscale basis transforms for signals on graphs
that utilize their "dual" domains by incorporating the "natural" distances
between graph Laplacian eigenvectors, rather than simply using the eigenvalue
ordering. These basis dictionaries can be seen as generalizations of the
classical Shannon wavelet packet dictionary to arbitrary graphs, and do not
rely on the frequency interpretation of Laplacian eigenvalues. We describe the
algorithms (involving either vector rotations or orthogonalizations) to
construct these basis dictionaries, use them to efficiently approximate graph
signals through the best basis search, and demonstrate the strengths of these
basis dictionaries for graph signals measured on sunflower graphs and street
networks
DivClust: Controlling Diversity in Deep Clustering
Clustering has been a major research topic in the field of machine learning,
one to which Deep Learning has recently been applied with significant success.
However, an aspect of clustering that is not addressed by existing deep
clustering methods, is that of efficiently producing multiple, diverse
partitionings for a given dataset. This is particularly important, as a diverse
set of base clusterings are necessary for consensus clustering, which has been
found to produce better and more robust results than relying on a single
clustering. To address this gap, we propose DivClust, a diversity controlling
loss that can be incorporated into existing deep clustering frameworks to
produce multiple clusterings with the desired degree of diversity. We conduct
experiments with multiple datasets and deep clustering frameworks and show
that: a) our method effectively controls diversity across frameworks and
datasets with very small additional computational cost, b) the sets of
clusterings learned by DivClust include solutions that significantly outperform
single-clustering baselines, and c) using an off-the-shelf consensus clustering
algorithm, DivClust produces consensus clustering solutions that consistently
outperform single-clustering baselines, effectively improving the performance
of the base deep clustering framework.Comment: Accepted for publication in CVPR 202
- …