Visual pattern discovery using random projections

Anand, Anushka; Wilkinson, Leland; Dang, Tommy

doi:10.1109/vast.2012.6400490

Cited by 33 publications

(31 citation statements)

References 44 publications

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“…TURES AND PARAMETERS REMAP is a client-server application that enables users to interactively explore and discover neural network architectures. 3 A screenshot of the tool can be seen in Figure 1. The interface features three components: a model overview represented by a scatter plot ( Fig.…”

Section: Remap: Rapid Exploration Of Model Architec-mentioning

confidence: 99%

Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architectures

Cashman

Perer

Chang

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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A B C Fig. 1: A screenshot of the REMAP system. In the model overview, section A, a visual overview of the set of sampled models is shown. Darkness of circles encodes performance of the models, and radius encodes the number of parameters. In the model drawer, section B, users can save models during their exploration for comparison or to return to later. In section C, four tabs help the user explore the model space and generate new models. The Generate Models tab, currently selected, allows for users to create new models via ablations, variations, or handcrafted templates.Abstract-Deep learning models require the configuration of many layers and parameters in order to get good results. However, there are currently few systematic guidelines for how to configure a successful model. This means model builders often have to experiment with different configurations by manually programming different architectures (which is tedious and time consuming) or rely on purely automated approaches to generate and train the architectures (which is expensive). In this paper, we present Rapid Exploration of Model Architectures and Parameters, or REMAP, a visual analytics tool that allows a model builder to discover a deep learning model quickly via exploration and rapid experimentation of neural network architectures. In REMAP, the user explores the large and complex parameter space for neural network architectures using a combination of global inspection and local experimentation. Through a visual overview of a set of models, the user identifies interesting clusters of architectures. Based on their findings, the user can run ablation and variation experiments to identify the effects of adding, removing, or replacing layers in a given architecture and generate new models accordingly. They can also handcraft new models using a simple graphical interface. As a result, a model builder can build deep learning models quickly, efficiently, and without manual programming. We inform the design of REMAP through a design study with four deep learning model builders. Through a use case, we demonstrate that REMAP allows users to discover performant neural network architectures efficiently using visual exploration and user-defined semi-automated searches through the model space.

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Section: Remap: Rapid Exploration Of Model Architec-mentioning

confidence: 99%

Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architectures

Cashman

Perer

Chang

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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“…Joia et al [14] introduced an advanced projection technique called local affine multidimensional projection (LAMP) to interactively correlate similar data instances. Anand et al [38] used random projection to find interesting substructures in a high-dimensional dataset. Although these approaches are capable of visualizing data by their low-dimensional layout, they are designed for data without uncertainty.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration

Chen

Zhang

Chen

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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Abstract-This paper presents an efficient visualization and exploration approach for modeling and characterizing the relationships and uncertainties in the context of multidimensional ensemble datasets. Its core is a novel dissimilarity-preserving projection technique that characterizes not only the relationships among the mean values of the ensemble data objects but also the relationships among the distributions of ensemble members. This uncertainty-aware projection scheme leads to an improved understanding of the intrinsic structure in an ensemble dataset. The analysis of the ensemble dataset is further augmented by a suite of visual encoding and exploration tools. Experimental results on both artificial and real-world datasets demonstrate the effectiveness of our approach.

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“…Existing subspace clustering methods can be algebraic, iterative, or spectral. Anand et al [AWD12] adopt random projections to help scale the projection pursuit to much larger dimensions. Analysis through Subsets of Dimensions.…”

Section: Related Workmentioning

confidence: 99%

Visual Exploration of High‐Dimensional Data through Subspace Analysis and Dynamic Projections

Liu

Wang

Thiagarajan

et al. 2015

Computer Graphics Forum

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We introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.

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Visual pattern discovery using random projections

Cited by 33 publications

References 44 publications

Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architectures

Ablate, Variate, and Contemplate: Visual Analytics for Discovering Neural Architectures

Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration

Visual Exploration of High‐Dimensional Data through Subspace Analysis and Dynamic Projections

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