scispace - formally typeset
Search or ask a question
Author

Ximing Li

Other affiliations: Chinese Ministry of Education
Bio: Ximing Li is an academic researcher from Jilin University. The author has contributed to research in topics: Computer science & Topic model. The author has an hindex of 13, co-authored 58 publications receiving 465 citations. Previous affiliations of Ximing Li include Chinese Ministry of Education.

Papers published on a yearly basis

Papers
More filters
Proceedings ArticleDOI
Hongtao Bai1, Dantong Ouyang1, Ximing Li1, Lili He1, Haihong Yu1 
07 Dec 2009
TL;DR: A parallel MAX-MIN Ant System (MMAS) algorithm that is suitable for an implementation on graphics processing units (GPUs) with compute unified device architecture (CUDA) is proposed.
Abstract: We propose a parallel MAX-MIN Ant System (MMAS) algorithm that is suitable for an implementation on graphics processing units (GPUs). Multi ant colonies with respective parameter settings are whole offloaded to the GPU in parallel. We have implemented this GPU-based MMAS on the GPU with compute unified device architecture (CUDA). Some performance optimization means for kernel program of GPU are introduced. Experimental results that are based on simulations for the traveling salesperson problem are presented to evaluate the proposed techniques.

59 citations

Journal ArticleDOI
Ximing Li1, Yue Wang1, Ang Zhang1, Changchun Li1, Jinjin Chi1, Jihong Ouyang1 
TL;DR: A common semantics topic model (CSTM) is proposed, which is a new type of topic, namely common topic, to gather the noise words in short texts to solve the sparsity and noise problems.

47 citations

Journal ArticleDOI
TL;DR: Two supervised topic models for multi-label classification problems are developed that outperform the state-of-the-art approaches and extend Latent Dirichlet Allocation (LDA) via two observations, i.e., the frequencies of the labels and the dependencies among different labels.

45 citations

Journal ArticleDOI
TL;DR: Experimental results show that weighting words can effectively improve the topic modeling performance over both short texts and normal long texts, and the proposed CEW significantly outperforms the existing term weighting schemes, since it further considers which words are informative.
Abstract: Topic models often produce unexplainable topics that are filled with noisy words. The reason is that words in topic modeling have equal weights. High frequency words dominate the top topic word lists, but most of them are meaningless words, e.g., domain-specific stopwords. To address this issue, in this paper we aim to investigate how to weight words, and then develop a straightforward but effective term weighting scheme, namely entropy weighting (EW). The proposed EW scheme is based on conditional entropy measured by word co-occurrences. Compared with existing term weighting schemes, the highlight of EW is that it can automatically reward informative words. For more robust word weight, we further suggest a combination form of EW (CEW) with two existing weighting schemes. Basically, our CEW assigns meaningless words lower weights and informative words higher weights, leading to more coherent topics during topic modeling inference. We apply CEW to Dirichlet multinomial mixture and latent Dirichlet allocation, and evaluate it by topic quality, document clustering and classification tasks on 8 real world data sets. Experimental results show that weighting words can effectively improve the topic modeling performance over both short texts and normal long texts. More importantly, the proposed CEW significantly outperforms the existing term weighting schemes, since it further considers which words are informative.

40 citations

Journal ArticleDOI
TL;DR: In LTM, the observable short texts are snippets of normal long texts generated by a given standard topic model, but their original document memberships are unknown, and with Gibbs sampling, LTM drives an adaptive aggregation process of short texts, and simultaneously estimates other latent variables of interest.
Abstract: Topic modeling for short texts faces a tough challenge, owing to the sparsity problem. An effective solution is to aggregate short texts into long pseudo-documents before training a standard topic model. The main concern of this solution is the way of aggregating short texts. A recent developed self-aggregation-based topic model (SATM) can adaptively aggregate short texts without using heuristic information. However, the model definition of SATM is a bit rigid, and more importantly, it tends to overfitting and time-consuming for large-scale corpora. To improve SATM, we propose a generalized topic model for short texts, namely latent topic model (LTM). In LTM, we assume that the observable short texts are snippets of normal long texts (namely original documents) generated by a given standard topic model, but their original document memberships are unknown. With Gibbs sampling, LTM drives an adaptive aggregation process of short texts, and simultaneously estimates other latent variables of interest. Additionally, we propose a mini-batch scheme for fast inference. Experimental results indicate that LTM is competitive with the state-of-the-art baseline models on short text topic modeling.

36 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling.
Abstract: Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.

608 citations

Posted Content
TL;DR: In this article, the authors investigated the research development, current trends and intellectual structure of topic modeling based on Latent Dirichlet Allocation (LDA), and summarized challenges and introduced famous tools and datasets in topic modelling based on LDA.
Abstract: Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data, text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modeling, which Latent Dirichlet allocation (LDA) is one of the most popular methods in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper can be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated scholarly articles highly (between 2003 to 2016) related to Topic Modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. Also, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.

546 citations