186 research outputs found
Maximum weighted likelihood via rival penalized EM for density mixture clustering with automatic model selection
Label-Noise Learning with Intrinsically Long-Tailed Data
Label noise is one of the key factors that lead to the poor generalization of
deep learning models. Existing label-noise learning methods usually assume that
the ground-truth classes of the training data are balanced. However, the
real-world data is often imbalanced, leading to the inconsistency between
observed and intrinsic class distribution with label noises. In this case, it
is hard to distinguish clean samples from noisy samples on the intrinsic tail
classes with the unknown intrinsic class distribution. In this paper, we
propose a learning framework for label-noise learning with intrinsically
long-tailed data. Specifically, we propose two-stage bi-dimensional sample
selection (TABASCO) to better separate clean samples from noisy samples,
especially for the tail classes. TABASCO consists of two new separation metrics
that complement each other to compensate for the limitation of using a single
metric in sample separation. Extensive experiments on benchmarks demonstrate
the effectiveness of our method. Our code is available at
https://github.com/Wakings/TABASCO.Comment: Accepted by ICCV 202
A Batch Rival Penalized Expectation-Maximization Algorithm for Gaussian Mixture Clustering with Automatic Model Selection
Within the learning framework of maximum weighted likelihood (MWL) proposed by Cheung, 2004 and 2005, this paper will develop a batch Rival Penalized Expectation-Maximization (RPEM) algorithm for density mixture clustering provided that all observations are available before the learning process. Compared to the adaptive RPEM algorithm in Cheung, 2004 and 2005, this batch RPEM need not assign the learning rate analogous to the Expectation-Maximization (EM) algorithm (Dempster et al., 1977), but still preserves the capability of automatic model selection. Further, the convergence speed of this batch RPEM is faster than the EM and the adaptive RPEM in general. The experiments show the superior performance of the proposed algorithm on the synthetic data and color image segmentation
Modeling Spatial Relations of Human Body Parts for Indexing and Retrieving Close Character Interactions
Retrieving pre-captured human motion for analyzing and synthesizing virtual character movement have been widely used in Virtual Reality (VR) and interactive computer graphics applications. In this paper, we propose a new human pose representation, called Spatial Relations of Human Body Parts (SRBP), to represent spatial relations between body parts of the subject(s), which intuitively describes how much the body parts are interacting with each other. Since SRBP is computed from the local structure (i.e. multiple body parts in proximity) of the pose instead of the information from individual or pairwise joints as in previous approaches, the new representation is robust to minor variations of individual joint location. Experimental results show that SRBP outperforms the existing skeleton-based motion retrieval and classification approaches on benchmark databases
Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine
The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs)
is that optimization objective functions will change with times or
environments. One of the promising approaches for solving the DMOPs is reusing
the obtained Pareto optimal set (POS) to train prediction models via machine
learning approaches. In this paper, we train an Incremental Support Vector
Machine (ISVM) classifier with the past POS, and then the solutions of the DMOP
we want to solve at the next moment are filtered through the trained ISVM
classifier. A high-quality initial population will be generated by the ISVM
classifier, and a variety of different types of population-based dynamic
multi-objective optimization algorithms can benefit from the population. To
verify this idea, we incorporate the proposed approach into three evolutionary
algorithms, the multi-objective particle swarm optimization(MOPSO),
Nondominated Sorting Genetic Algorithm II (NSGA-II), and the Regularity
Model-based multi-objective estimation of distribution algorithm(RE-MEDA). We
employ experiments to test these algorithms, and experimental results show the
effectiveness.Comment: 6 page
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