jmlr jmlr2013 jmlr2013-37 jmlr2013-37-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten
Abstract: Divvy is an application for applying unsupervised machine learning techniques (clustering and dimensionality reduction) to the data analysis process. Divvy provides a novel UI that allows researchers to tighten the action-perception loop of changing algorithm parameters and seeing a visualization of the result. Machine learning researchers can use Divvy to publish easy to use reference implementations of their algorithms, which helps the machine learning field have a greater impact on research practices elsewhere. Keywords: clustering, dimensionality reduction, open source software, human computer interaction, data visualization
Ayasdi, Inc. Iris: Query-free insight discovery, 2013. URL http://www.ayasdi.com/product/. 1. Overall the Divvy codebase is two-thirds Objective-C (interface, plugin wrappers) and one-third C/C++ (algorithms). As algorithms are added, this balance will shift towards platform-independent code. To port Divvy to another platform, one would only need to rewrite the interface code. 2. Joshua Lewis wrote the entire Divvy application and bundled plugins save the dimensionality reduction plugins, which were written by Laurens van der Maaten. 3. With twelve cores and two threads per core, peak utilization is 2,400%. 4. We picked libdispatch because OpenMP has an issue with GCC 4.2/4.3 where starting a parallel section from a thread other than the main thread causes a crash, so we cannot recommend it. 3162 D IVVY: FAST AND I NTUITIVE E XPLORATORY DATA A NALYSIS Deborah F. Swayne, Duncan Temple Lang, Andreas Buja, and Dianne Cook. GGobi: evolving from XGobi into an extensible framework for interactive data visualization. Computational Statistics & Data Analysis, 43:423–444, 2003. Joshua B. Tenenbaum, Vin De Silva, and John C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 2000. University of Ljubljana Bioinformatics Laboratory. Orange - data mining fruitful and fun, 2013. URL http://orange.biolab.si. Laurens van der Maaten and Geoffrey Hinton. Visualizing Data using t-SNE. Journal of Machine Learning Research, 9:2579–2605, November 2008. 3163