In crowdsourcing scenarios,we can obtain each instance's multiple noisy labels from different crowd workers and then infer its integrated label via label aggregation.In spite of the effectiveness of label aggregat...In crowdsourcing scenarios,we can obtain each instance's multiple noisy labels from different crowd workers and then infer its integrated label via label aggregation.In spite of the effectiveness of label aggregation methods,there still remains a certain level of noise in the integrated labels.Thus,some noise correction methods have been proposed to reduce the impact of noise in recent years.However,to the best of our knowledge,existing methods rarely consider an instance's information from both its features and multiple noisy labels simultaneously when identifying a noise instance.In this study,we argue that the more distinguishable an instance's features but the noisier its multiple noisy labels,the more likely it is a noise instance.Based on this premise,we propose a label distribution similarity-based noisecorrection(LDSNC)method.To measure whether an instance's features are distinguishable,we obtain each instance's predicted label distribution by building multiple classifiers using instances'features and their integrated labels.To measure whether an instance's multiple noisy labels are noisy,we obtain each instance's multiple noisy label distribution using its multiple noisy labels.Then,we use the Kullback-Leibler(KL)divergence to calculate the similarity between the predicted label distribution and multiple noisy label distribution and define the instance with the lower similarity as a noise instance.The extensive experimental results on 34 simulated and four real-world crowdsourced datasets validate the effectiveness of our method.展开更多
In this paper, we present our research on building computing machines consciousness about intuitive geometry based on mathematics experiments and statistical inference. The investigation consists of the following five...In this paper, we present our research on building computing machines consciousness about intuitive geometry based on mathematics experiments and statistical inference. The investigation consists of the following five steps. At first, we select a set of geometric configurations and for each configuration we construct a large amount of geometric data as observation data using dynamic geometry programs together with the pseudo-random number generator. Secondly, we refer to the geometric predicates in the algebraic method of machine proof of geometric theorems to construct statistics suitable for measuring the approximate geometric relationships in the observation data. In the third step, we propose a geometric relationship detection method based on the similarity of data distribution, where the search space has been reduced into small batches of data by pre-searching for efficiency, and the hypothetical test of the possible geometric relationships in the search results has be performed. In the fourth step, we explore the integer relation of the line segment lengths in the geometric configuration in addition. At the final step, we do numerical experiments for the pre-selected geometric configurations to verify the effectiveness of our method. The results show that computer equipped with the above procedures can find out the hidden geometric relations from the randomly generated data of related geometric configurations, and in this sense, computing machines can actually attain certain consciousness of intuitive geometry as early civilized humans in ancient Mesopotamia.展开更多
Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dyna...Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.展开更多
基金The work was partially supported by the National Natural Science Foundation of China(Grant No.62276241)Foundation of Key Laboratory of Artificial Intelligence,Ministry of Education,China(AI2022004).
文摘In crowdsourcing scenarios,we can obtain each instance's multiple noisy labels from different crowd workers and then infer its integrated label via label aggregation.In spite of the effectiveness of label aggregation methods,there still remains a certain level of noise in the integrated labels.Thus,some noise correction methods have been proposed to reduce the impact of noise in recent years.However,to the best of our knowledge,existing methods rarely consider an instance's information from both its features and multiple noisy labels simultaneously when identifying a noise instance.In this study,we argue that the more distinguishable an instance's features but the noisier its multiple noisy labels,the more likely it is a noise instance.Based on this premise,we propose a label distribution similarity-based noisecorrection(LDSNC)method.To measure whether an instance's features are distinguishable,we obtain each instance's predicted label distribution by building multiple classifiers using instances'features and their integrated labels.To measure whether an instance's multiple noisy labels are noisy,we obtain each instance's multiple noisy label distribution using its multiple noisy labels.Then,we use the Kullback-Leibler(KL)divergence to calculate the similarity between the predicted label distribution and multiple noisy label distribution and define the instance with the lower similarity as a noise instance.The extensive experimental results on 34 simulated and four real-world crowdsourced datasets validate the effectiveness of our method.
文摘In this paper, we present our research on building computing machines consciousness about intuitive geometry based on mathematics experiments and statistical inference. The investigation consists of the following five steps. At first, we select a set of geometric configurations and for each configuration we construct a large amount of geometric data as observation data using dynamic geometry programs together with the pseudo-random number generator. Secondly, we refer to the geometric predicates in the algebraic method of machine proof of geometric theorems to construct statistics suitable for measuring the approximate geometric relationships in the observation data. In the third step, we propose a geometric relationship detection method based on the similarity of data distribution, where the search space has been reduced into small batches of data by pre-searching for efficiency, and the hypothetical test of the possible geometric relationships in the search results has be performed. In the fourth step, we explore the integer relation of the line segment lengths in the geometric configuration in addition. At the final step, we do numerical experiments for the pre-selected geometric configurations to verify the effectiveness of our method. The results show that computer equipped with the above procedures can find out the hidden geometric relations from the randomly generated data of related geometric configurations, and in this sense, computing machines can actually attain certain consciousness of intuitive geometry as early civilized humans in ancient Mesopotamia.
基金supported by the Innovation Project of Graduate Students of Jiangsu Province, China under Grants No. CXZZ12_0466, No. CXZZ11_0390the National Natural Science Foundation of China under Grants No. 61071091, No. 61271240, No. 61201160, No. 61172118+2 种基金the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China under Grant No. 12KJB510019the Science and Technology Research Program of Hubei Provincial Department of Education under Grants No. D20121408, No. D20121402the Program for Research Innovation of Nanjing Institute of Technology Project under Grant No. CKJ20110006
文摘Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.
