Weizheng Chen

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Biterm Topic Model (BTM) is designed to model the generative process of the word co-occurrence patterns in short texts such as tweets. However, two aspects of BTM may restrict its performance: 1) user individualities are ignored to obtain the corpus level words co-occurrence patterns; and 2) the strong assumptions that two co-occurring words will be(More)
Single cell RNA-seq (scRNA-seq) techniques can reveal valuable insights of cell-to-cell heterogeneities. Projection of high-dimensional data into a low-dimensional subspace is a powerful strategy in general for mining such big data. However, scRNA-seq suffers from higher noise and lower coverage than traditional bulk RNA-seq, hence bringing in new(More)
Network embedding is a classical task which aims to map the nodes of a network to lowdimensional vectors. Most of the previous network embedding methods are trained in an unsupervised scheme. Then the learned node embeddings can be used as inputs of many machine learning tasks such as node classification, attribute inference. However, the discriminant power(More)
This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive polar of all words. After that, we(More)
It is a great challenge to model and mine the e-commercial data, which is made up of multiple types of objects, such as products, users, comments and tags. To model the complicated interactive relationships in the the e-commercial data, we propose to transform the complex e-commercial data into a text-rich heterogeneous e-commercial network. Then three(More)
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