Qingyang Li, Zhiwen Yu, Bin Guo, Huang Xu, Xinjiang Lu
Housing demand estimation is an important topic in the field of economic research. It is beneficial and helpful for various applications including real estate market regulation and urban planning, and therefore is crucial for both real estate investors and government administrators. Meanwhile, given the rapid development of the express industry, abundant useful information is embedded in express delivery records, which is helpful for researchers in profiling urban life patterns. The express delivery behaviors of the residents in a residential community can reflect the housing demand to some extent. Although housing demand has been analyzed in previous studies, its estimation has not been very good, and the subject remains under explored.
Category: ACM Transactions on Knowledge Discovery from Data (TKDD)
Time-Sync Video Tag Extraction Using Semantic Association Graph
Wenmain Yang, Kun Wang, Na Ruan, Wenyuan Gao, Weijia Jia, Wei Zhao, Nan Liu, Yunyong Zhang
Time-sync comments (TSCs) reveal a new way of extracting the online video tags. However, such TSCs have lots of noises due to users’ diverse comments, introducing great challenges for accurate and fast video tag extractions. In this article, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding semantic association graph (SAG) using semantic similarities and timestamps of the TSCs. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises.
Time-sync comments (TSCs) reveal a new way of extracting the online video tags. However, such TSCs have lots of noises due to users’ diverse comments, introducing great challenges for accurate and fast video tag extractions. In this article, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding semantic association graph (SAG) using semantic similarities and timestamps of the TSCs. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises.
Active Two Phase Collaborative Representation Classifier
Fadi Dornaika
The Sparse Representation Classifier, the Collaborative Representation Classifier (CRC), and the Two Phase Test Sample Sparse Representation (TPTSSR) classifier were introduced in recent times. All these frameworks are supervised and passive in the sense that they cannot benefit from unlabeled data samples. In this paper, inspired by active learning paradigms, we introduce an active CRC that can be used by these frameworks. More precisely, we are interested in the TPTSSR framework due to its good performance and its reasonable computational cost. Our proposed Active Two Phase Collaborative Representation Classifier (ATPCRC) starts by predicting the label of the available unlabeled samples. At testing stage, two coding processes are carried out separately on the set of originally labeled samples and the whole set (original and predicted label).
The Sparse Representation Classifier, the Collaborative Representation Classifier (CRC), and the Two Phase Test Sample Sparse Representation (TPTSSR) classifier were introduced in recent times. All these frameworks are supervised and passive in the sense that they cannot benefit from unlabeled data samples. In this paper, inspired by active learning paradigms, we introduce an active CRC that can be used by these frameworks. More precisely, we are interested in the TPTSSR framework due to its good performance and its reasonable computational cost. Our proposed Active Two Phase Collaborative Representation Classifier (ATPCRC) starts by predicting the label of the available unlabeled samples. At testing stage, two coding processes are carried out separately on the set of originally labeled samples and the whole set (original and predicted label).
Time-Sync Video Tag Extraction Using Semantic Association Graph
Wenmain Yang, Kun Wang, Na Ruan, Wenyuan Gao, Weijia Jia, Wei Zhao, Nan Liu, Yunyong Zhang
Time-sync comments (TSCs) reveal a new way of extracting the online video tags. However, such TSCs have lots of noises due to users’ diverse comments, introducing great challenges for accurate and fast video tag extractions. In this article, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding semantic association graph (SAG) using semantic similarities and timestamps of the TSCs. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises.
Time-sync comments (TSCs) reveal a new way of extracting the online video tags. However, such TSCs have lots of noises due to users’ diverse comments, introducing great challenges for accurate and fast video tag extractions. In this article, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding semantic association graph (SAG) using semantic similarities and timestamps of the TSCs. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises.
Active Two Phase Collaborative Representation Classifier
Fadi Dornaika
The Sparse Representation Classifier, the Collaborative Representation Classifier (CRC), and the Two Phase Test Sample Sparse Representation (TPTSSR) classifier were introduced in recent times. All these frameworks are supervised and passive in the sense that they cannot benefit from unlabeled data samples. In this paper, inspired by active learning paradigms, we introduce an active CRC that can be used by these frameworks. More precisely, we are interested in the TPTSSR framework due to its good performance and its reasonable computational cost. Our proposed Active Two Phase Collaborative Representation Classifier (ATPCRC) starts by predicting the label of the available unlabeled samples. At testing stage, two coding processes are carried out separately on the set of originally labeled samples and the whole set (original and predicted label).
The Sparse Representation Classifier, the Collaborative Representation Classifier (CRC), and the Two Phase Test Sample Sparse Representation (TPTSSR) classifier were introduced in recent times. All these frameworks are supervised and passive in the sense that they cannot benefit from unlabeled data samples. In this paper, inspired by active learning paradigms, we introduce an active CRC that can be used by these frameworks. More precisely, we are interested in the TPTSSR framework due to its good performance and its reasonable computational cost. Our proposed Active Two Phase Collaborative Representation Classifier (ATPCRC) starts by predicting the label of the available unlabeled samples. At testing stage, two coding processes are carried out separately on the set of originally labeled samples and the whole set (original and predicted label).