Kolmogorov complexity and its applications

我对开发一个引人注目的大数据理论很感兴趣。这种理论将取决于柯尔莫哥洛夫复杂度和信息距离。Kolmogorov复杂度定义在一个对象上。信息距离[C]。张晓明，李晓明，张晓明，张晓明。信息距离的概念与方法[j] .信息学报，2016,44(4):555 - 557。这个概念可以推广到许多对象。一般来说，使用这样的理论可以最优地近似直观的“语义距离”概念或两个数据块的接近度。这个理论的关键是压缩数据。许多压缩数据的方法将被研究，包括错误编码，聚类，特别是深度神经网络。深度神经网络可以看作是一种压缩数据，尤其是大数据的方法。以下短期目标与本研究的上述主题一致：***1)自然语言处理（NLP）中的深度学习。我的团队训练了一个卷积神经网络（CNN），将自然语言问题映射到具有有限数量关系的数据库结构化查询。这项工作将继续下去。我的小组还训练了一个用于对话或聊天的循环神经网络（RNN）。这项工作将扩展到上下文敏感的聊天。这项工作将对长期目标产生两个影响：a)神经网络将被研究为近似语义距离的一种方法；b)只有使用来自互联网的大数据，这种方法才实际有用。* * * 2)生物信息学。CNN还被训练用于蛋白质鉴定以及质谱蛋白质定量中的峰拾取。这些研究和方法将扩展到蛋白质定量。这项工作同样依赖于我从行业中获得的大量培训数据。这些深度学习方法不会被孤立地研究。它们将与我的信息距离逼近语义距离的理论一起研究，在没有明确压缩规则的情况下，实验使用深度神经网络作为压缩方法处理大数据的效率。我还将花8个月的时间与Paul Vitanyi一起修改他的研究著作《Kolmogorov复杂性及其应用的介绍》，其中将包括这些新的结果。***将研究生物信息学的其他几个短期课题。一个是抗体测序算法。我计划用线性规划设计一种新的算法来解决抗体测序的生物信息学工业问题。另一个问题是将生物信息学的思想应用到其他领域：我的团队发明了最佳间隔种子来进行同源性搜索。这被认为是过去15年来生物信息学领域最具影响力的创新之一。我想用最优间隔种子的想法来发展一种观察理论来检测时间序列的趋势。初步实验成功

I am interested in developing a compelling theory of big data. Such a theory will depend on Kolmogorov complexity and information distance. Kolmogorov complexity is defined on one object. Information distance [C. Bennett, P. Gacs, M. Li, P. Vitanyi, W. Zurek, Information distance, IEEE Tran-IT, 44:4(1998)] is defined on two objects. This concept can be generalized to many objects. Using such a theory it is possible to optimally approximate the intuitive concept of "semantic distance" or closeness of two piece of data, in general. The key to this theory is to compress the data. Many ways of compressing data will be studied, including error encoding, clustering, and especially deep neural networks. Deep neural networks can be considered as ways of compressing data, especially big data. The following short-term goals are in tune with the above main theme of this research: ***1) Deep learning in natural language processing (NLP). My group has trained a Convolutional Neural Network (CNN) to map natural language questions to a database structured query with a limited number of relations. This work will continue. My group also has trained a Recurrent Neural Network (RNN) for conversation or chatting. This work will be extended to context sensitive chatting. This work will have two implications with the long term goal: a) Neural network will be studied as one way to approximate semantic distance; and b) Only with big data from the internet, this approach is practically useful.***2) Bioinformatics. A CNN has also been trained for protein identification as well as for peak-picking in mass spectrometry protein quantitation. These studies and methodologies will be extended to protein quantitation. This work again depends on huge amount of training data I have obtained from industry. ***These deep learning approaches will not be studied in isolation. They will be studied together with my theory of approximating semantic distance by information distance, experimenting with the efficiency of using deep neural networks as compression methods to deal with big data when there are no clear rules of compressing. I will also spend 8 months full time to revise his research book with Paul Vitanyi "An introduction to Kolmogorov complexity and its applications", that will include these new results. ***Several other short-term topics in bioinformatics will be studied. One is an antibody sequencing algorithm. I plan to design a new algorithm using linear programming to solve a bioinformatics industrial problem of antibody sequencing. Another problem is to apply the ideas in bioinformatics to other fields: optimal spaced seeds were invented by my group to do homology search. This has been considered one of the most influential innovations in bioinformatics during the last 15 years. I have the idea of using the optimal spaced seed idea to develop an observation theory to detect the trends in time series. Initial experiments were performed successfully.**