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Hanmo Wang

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6 papers

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AAAI Conference 2019 Conference Paper

Bounding Uncertainty for Active Batch Selection

Hanmo Wang
Runwu Zhou
Yi-Dong Shen

The success of batch mode active learning (BMAL) methods lies in selecting both representative and uncertain samples. Representative samples quickly capture the global structure of the whole dataset, while the uncertain ones refine the decision boundary. There are two principles, namely the direct approach and the screening approach, to make a trade-off between representativeness and uncertainty. Although widely used in literature, little is known about the relationship between these two principles. In this paper, we discover that the two approaches both have shortcomings in the initial stage of BMAL. To alleviate the shortcomings, we bound the certainty scores of unlabeled samples from below and directly combine this lower-bounded certainty with representativeness in the objective function. Additionally, we show that the two aforementioned approaches are mathematically equivalent to two special cases of our approach. To the best of our knowledge, this is the first work that tries to generalize the direct and screening approaches. The objective function is then solved by super-modularity optimization. Extensive experiments on fifteen datasets indicate that our method has significantly higher classification accuracy on testing data than the latest state-of-the-art BMAL methods, and also scales better even when the size of the unlabeled pool reaches 106.

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IJCAI Conference 2018 Conference Paper

Uncertainty Sampling for Action Recognition via Maximizing Expected Average Precision

Hanmo Wang
Xiaojun Chang
Lei Shi
Yi Yang
Yi-Dong Shen

Recognizing human actions in video clips has been an important topic in computer vision. Sufficient labeled data is one of the prerequisites for the good performance of action recognition algorithms. However, while abundant videos can be collected from the Internet, categorizing each video clip is tedious and even time-consuming. Active learning is one way to alleviate the labeling labor by allowing the classifier to choose the most informative unlabeled instances for manual annotation. Among various active learning algorithms, uncertainty sampling is arguably the most widely-used strategy. Conventional uncertainty sampling strategies such as entropy-based methods are usually tested under accuracy. However, in action recognition Average Precision (AP) is an acknowledged evaluation metric, which is somehow ignored in the active learning community. It is defined as the area under the precision-recall curve. In this paper, we propose a novel uncertainty sampling algorithm for action recognition using expected AP. We conduct experiments on three real-world action recognition datasets and show that our algorithm outperforms other uncertainty-based active learning algorithms.

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AAAI Conference 2015 Conference Paper

Convex Batch Mode Active Sampling via α-Relative Pearson Divergence

Hanmo Wang
Liang Du
Peng Zhou
Lei Shi
Yi-Dong Shen

Active learning is a machine learning technique that trains a classifier after selecting a subset from an unlabeled dataset for labeling and using the selected data for training. Recently, batch mode active learning, which selects a batch of samples to label in parallel, has attracted a lot of attention. Its challenge lies in the choice of criteria used for guiding the search of the optimal batch. In this paper, we propose a novel approach to selecting the optimal batch of queries by minimizing the α-relative Pearson divergence (RPE) between the labeled and the original datasets. This particular divergence is chosen since it can distinguish the optimal batch more easily than other measures especially when available candidates are similar. The proposed objective is a min-max optimization problem, and it is difficult to solve due to the involvement of both minimization and maximization. We find that the objective has an equivalent convex form, and thus a global optimal solution can be obtained. Then the subgradient method can be applied to solve the simplified convex problem. Our empirical studies on UCI datasets demonstrate the effectiveness of the proposed approach compared with the state-of-the-art batch mode active learning methods.

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IJCAI Conference 2015 Conference Paper

Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization

Peng Zhou
Liang Du
Hanmo Wang
Lei Shi
Yi-Dong Shen

Clustering ensemble has emerged as an important extension of the classical clustering problem. It provides a framework for combining multiple base clusterings of a data set to generate a final consensus result. Most existing clustering methods simply combine clustering results without taking into account the noises, which may degrade the clustering performance. In this paper, we propose a novel robust clustering ensemble method. To improve the robustness, we capture the sparse and symmetric errors and integrate them into our robust and consensus framework to learn a low-rank matrix. Since the optimization of the objective function is difficult to solve, we develop a block coordinate descent algorithm which is theoretically guaranteed to converge. Experimental results on real world data sets demonstrate the effectiveness of our method.

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IJCAI Conference 2015 Conference Paper

Recovery of Corrupted Multiple Kernels for Clustering

Peng Zhou
Liang Du
Lei Shi
Hanmo Wang
Yi-Dong Shen

Kernel-based methods, such as kernel k-means and kernel PCA, have been widely used in machine learning tasks. The performance of these methods critically depends on the selection of kernel functions; however, the challenge is that we usually do not know what kind of kernels is suitable for the given data and task in advance; this leads to research on multiple kernel learning, i. e. we learn a consensus kernel from multiple candidate kernels. Existing multiple kernel learning methods have difficulty in dealing with noises. In this paper, we propose a novel method for learning a robust yet lowrank kernel for clustering tasks. We observe that the noises of each kernel have specific structures, so we can make full use of them to clean multiple input kernels and then aggregate them into a robust, low-rank consensus kernel. The underlying optimization problem is hard to solve and we will show that it can be solved via alternating minimization, whose convergence is theoretically guaranteed. Experimental results on several benchmark data sets further demonstrate the effectiveness of our method.

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IJCAI Conference 2015 Conference Paper

Robust Multiple Kernel K-means Using L21-Norm

Liang Du
Peng Zhou
Lei Shi
Hanmo Wang
Mingyu Fan
Wenjian Wang
Yi-Dong Shen

The k-means algorithm is one of the most often used method for data clustering. However, the standard k-means can only be applied in the original feature space. The kernel k-means, which extends k-means into the kernel space, can be used to capture the non-linear structure and identify arbitrarily shaped clusters. Since both the standard k-means and kernel k-means apply the squared error to measure the distances between data points and cluster centers, a few outliers will cause large errors and dominate the objection function. Besides, the performance of kernel method is largely determined by the choice of kernel. Unfortunately, the most suitable kernel for a particular task is often unknown in advance. In this paper, we first present a robust kmeans using `2, 1-norm in the feature space and then extend it to the kernel space. To recap the powerfulness of kernel methods, we further propose a novel robust multiple kernel k-means (RMKKM) algorithm that simultaneously finds the best clustering label, the cluster membership and the optimal combination of multiple kernels. An alternating iterative schema is developed to find the optimal value. Extensive experiments well demonstrate the effectiveness of the proposed algorithms.

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