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Multi-View Collaborative Representation Classification
Department of Finance and Education of Guangdong Province 2016 [202]: Key Discipline Construction Program, China; Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022]
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts
Graph classification, aiming at learning the graph-level representations for
effective class assignments, has received outstanding achievements, which
heavily relies on high-quality datasets that have balanced class distribution.
In fact, most real-world graph data naturally presents a long-tailed form,
where the head classes occupy much more samples than the tail classes, it thus
is essential to study the graph-level classification over long-tailed data
while still remaining largely unexplored. However, most existing long-tailed
learning methods in visions fail to jointly optimize the representation
learning and classifier training, as well as neglect the mining of the
hard-to-classify classes. Directly applying existing methods to graphs may lead
to sub-optimal performance, since the model trained on graphs would be more
sensitive to the long-tailed distribution due to the complex topological
characteristics. Hence, in this paper, we propose a novel long-tailed
graph-level classification framework via Collaborative Multi-expert Learning
(CoMe) to tackle the problem. To equilibrate the contributions of head and tail
classes, we first develop balanced contrastive learning from the view of
representation learning, and then design an individual-expert classifier
training based on hard class mining. In addition, we execute gated fusion and
disentangled knowledge distillation among the multiple experts to promote the
collaboration in a multi-expert framework. Comprehensive experiments are
performed on seven widely-used benchmark datasets to demonstrate the
superiority of our method CoMe over state-of-the-art baselines.Comment: Accepted by IEEE Transactions on Big Data (TBD 2024
Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors
Promising results have been achieved in image classification problems by
exploiting the discriminative power of sparse representations for
classification (SRC). Recently, it has been shown that the use of
\emph{class-specific} spike-and-slab priors in conjunction with the
class-specific dictionaries from SRC is particularly effective in low training
scenarios. As a logical extension, we build on this framework for multitask
scenarios, wherein multiple representations of the same physical phenomena are
available. We experimentally demonstrate the benefits of mining joint
information from different camera views for multi-view face recognition.Comment: Accepted to International Conference in Image Processing (ICIP) 201
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
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