Paper Presentation (Google Drive)
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ECCV 2014 Visualizing and Understanding Convolutional Networks
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TCSVT 2012 Empirical Studies in Information Visualization Seven Scenarios
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TCSVT 2013 Feature Driven Visual Analytics of Soccer Data
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TCSVT 2013 Perceptually Driven Visibility Optimization for Categorical Data Visualization
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TCSVT 2013 Temporal MDS Plots for Anlaysis of Multivariate Data
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TCSVT 2013 TimeSeer Scagnositcs for High-Dimensional Time Series
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TCSVT 2013 Visualizing Natural Image Statistics
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TCSVT 2014 Visual Abstraction and Exploration of Multi-class Scatterplots
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TCSVT 2014 Visual Methods for Analyzing Probabilistic Classification Data
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TCSVT 2016 Spatial Reasoning and Data Displays
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TCSVT 2016 Temporal MDS Plots for Analysis of Multivariate Data
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TCSVT 2017 Clustervision Visual Supervision of Unsupervised Clustering
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TCSVT 2017 Do Convolutional Neural Networks Learn Class Hierarchy
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TCSVT 2017 LDSScanner Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets
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TCSVT 2017 Visual Methods for Anlayzing Probabilistic Classification Data
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TCSVT 2017 What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization
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VAST 2011 Observation-Level Interaction with Statistical Models for Visual Analytics
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VIS 2017 Visual Integration of Data and Model Space in Ensemlbe Learning
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VIS 2017 Visualization of Big Spatial Data using Coresets for Kernel Density Estimates
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TCSVT 2016 Optimal Sets of Projections of High-Dimensional Data
Assignment 1. Dimension Reduction and Clustering Algorithms
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Choose one dimension reduction or clustering algorithm to implement using c/c++, python, matlab
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All core algorithms should be written by yourself, except the basic matrix operations, such as SVD decomposition
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When submit the assignment, the readme.txt should write what dataset has been used and how to run your codes
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Here are some recommaned datasets. http://web.mit.edu/cocosci/isomap/datasets.html and https://drive.google.com/drive/u/1/folders/0B_P65v9k7Nmed2JPVWNlV3hYYVE