Interdisciplinary Studies of Complex Network and Machine Learning and Its Applications

<p>In this dissertation, we introduce the concept of network-based statistical inference methods of two types: network structure inference and variable inference. For network structure inference, we introduce correlation matrix, graphical Lasso, network clustering and identify the influencer in the network. For variable inference, we also introduce from Bayesian network, to Random Markov Field and Ising Model, Boltzmann and Restricted Boltzmann machine and the algorithm of Belief Propagation. Last but not the least, we introduce the most widely used neural network family and its two main types: Convolutional Neural Network and Recurrent Neural Network.</p> <p>In Chapter 3 we provide an example of applying network structure inference algorithm to find the correlation between network metrics and socio-economic stats are introduced in this chapter. A mobile network with 108 nodes were constructed from mobile records to build the social networks by filtering the abnormal phone lines with a semi-supervised learning. Collect Influence (CI) is used as the proxy of network influence. A novel correlation (R2 = 0:95) is achieved by investigating the correlation between aggregated population which is based on both age and network metrics quantile. The result is validate by a marketing campaign.</p> <p>In Chapter 4, we provide an example of combining large scale neural networks</p> <p>to build a deep learning workflow to predict the pathology result of breast tumor based on MRI images. The work flow consist of three agents: Feature Extraction Agent which is a deep convolutional neural network transferred from inception v3. Image Selection Agent is a bi-directed recurrent neural network which evaluate the score of risk for each slice window and a Pathology Prediction Agent is to predict the pathology result based on the slices windows based on selection agent. The work flow is trained by reinforcement learning in order to automatically detect the location of tumor. Although the result indicates the workflow is able to capture the evidence of malignancy, the workflow still needs to be improve to increase stability.</p>

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Physics

Grantor

The Graduate School and University Center of The City University of New York

Year dc:date.available

2018

Author dc:creator

• Luo, Shaojun

Advisor dc:contributor.advisor

• Hernan Makse

Committee members dc:contributor.committeemember

• Robert Haralick
• Flaviano Morone
• Gino Del Ferraro
• Lucas Parra

• Statistical, Nonlinear, and Soft Matter Physics
• Network Science
• Statistical Inference
• Machine Learning
• Socio-economy
• Breast Cancer

Repository record dc:identifier

https://academicworks.cuny.edu/gc_etds/2846

OAI identifier oai:identifier

oai:academicworks.cuny.edu:gc_etds-3902