7,803 research outputs found
Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation
This article presents Individual Conditional Expectation (ICE) plots, a tool
for visualizing the model estimated by any supervised learning algorithm.
Classical partial dependence plots (PDPs) help visualize the average partial
relationship between the predicted response and one or more features. In the
presence of substantial interaction effects, the partial response relationship
can be heterogeneous. Thus, an average curve, such as the PDP, can obfuscate
the complexity of the modeled relationship. Accordingly, ICE plots refine the
partial dependence plot by graphing the functional relationship between the
predicted response and the feature for individual observations. Specifically,
ICE plots highlight the variation in the fitted values across the range of a
covariate, suggesting where and to what extent heterogeneities might exist. In
addition to providing a plotting suite for exploratory analysis, we include a
visual test for additive structure in the data generating model. Through
simulated examples and real data sets, we demonstrate how ICE plots can shed
light on estimated models in ways PDPs cannot. Procedures outlined are
available in the R package ICEbox.Comment: 22 pages, 14 figures, 2 algorithm
NodeTrix: Hybrid Representation for Analyzing Social Networks
The need to visualize large social networks is growing as hardware
capabilities make analyzing large networks feasible and many new data sets
become available. Unfortunately, the visualizations in existing systems do not
satisfactorily answer the basic dilemma of being readable both for the global
structure of the network and also for detailed analysis of local communities.
To address this problem, we present NodeTrix, a hybrid representation for
networks that combines the advantages of two traditional representations:
node-link diagrams are used to show the global structure of a network, while
arbitrary portions of the network can be shown as adjacency matrices to better
support the analysis of communities. A key contribution is a set of interaction
techniques. These allow analysts to create a NodeTrix visualization by dragging
selections from either a node-link or a matrix, flexibly manipulate the
NodeTrix representation to explore the dataset, and create meaningful summary
visualizations of their findings. Finally, we present a case study applying
NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate
the capabilities of NodeTrix as both an exploration tool and an effective means
of communicating results
Generative Adversarial Mapping Networks
Generative Adversarial Networks (GANs) have shown impressive performance in
generating photo-realistic images. They fit generative models by minimizing
certain distance measure between the real image distribution and the generated
data distribution. Several distance measures have been used, such as
Jensen-Shannon divergence, -divergence, and Wasserstein distance, and
choosing an appropriate distance measure is very important for training the
generative network. In this paper, we choose to use the maximum mean
discrepancy (MMD) as the distance metric, which has several nice theoretical
guarantees. In fact, generative moment matching network (GMMN) (Li, Swersky,
and Zemel 2015) is such a generative model which contains only one generator
network trained by directly minimizing MMD between the real and generated
distributions. However, it fails to generate meaningful samples on challenging
benchmark datasets, such as CIFAR-10 and LSUN. To improve on GMMN, we propose
to add an extra network , called mapper. maps both real data
distribution and generated data distribution from the original data space to a
feature representation space , and it is trained to maximize MMD
between the two mapped distributions in , while the generator
tries to minimize the MMD. We call the new model generative adversarial mapping
networks (GAMNs). We demonstrate that the adversarial mapper can help
to better capture the underlying data distribution. We also show that GAMN
significantly outperforms GMMN, and is also superior to or comparable with
other state-of-the-art GAN based methods on MNIST, CIFAR-10 and LSUN-Bedrooms
datasets.Comment: 9 pages, 7 figure
DOLPHIN: the design and initial evaluation of multimodal focus and context
In this paper we describe a new focus and context visualisation technique called multimodal focus and context. This technique uses a hybrid visual and spatialised audio display space to overcome the limited visual displays of mobile devices. We demonstrate this technique by applying it to maps of theme parks. We present the results of an experiment comparing multimodal focus and context to a purely visual display technique. The results showed that neither system was significantly better than the other. We believe that this is due to issues involving the perception of multiple structured audio sources
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