Visualizing dimensionality reduction of systems biology data
نویسندگان
چکیده
منابع مشابه
Distributed Spectral Dimensionality Reduction for Visualizing Textual Data
We use a Spectral Clustering model to formulate a distributed implementation using SPARK of Laplacian Eigenmaps that we call Distributed Spectral Dimensionality Reduction (DSDR). We evaluate DSDR to visualize conceptual clusters of terms in textual data from 2149 short documents written by online contributors to a State Department website. We compare DSDR with PCA, MultiDimensional Scaling, ISO...
متن کاملVisualizing the quality of dimensionality reduction
Many different evaluation measures for dimensionality reduction can be summarized based on the co-ranking framework [6]. Here, we extend this framework in two ways: (i) we show that the current parameterization of the quality shows unpredictable behavior, even in simple settings, and we propose a different parameterization which yields more intuitive results; (ii) we propose how to link the qua...
متن کاملVisualizing Dimensionality Reduction Artifacts: An Evaluation
Multidimensional scaling allows visualizing high-dimensional data as 2D maps with the premise that insights in 2D reveal valid information in high-dimensions, but the resulting projections always suffer from artifacts such as false neighborhoods and tears. These artifacts can be revealed by interactively coloring the projection according to the original dissimilarities relative to a reference i...
متن کاملIntrinsic Dimensionality Estimation in Visualizing Toxicity Data
Over the years, a number of dimensionality reduction techniques have been proposed and used in chemo informatics to perform nonlinear mappings. Nevertheless, data visualization techniques can be efficiently applied for dimensionality reduction mainly in a case if the data are not really high-dimensional and can be represented as a nonlinear low-dimensional manifold when it is possible to reduce...
متن کاملDimensionality Reduction for Data Visualization
Dimensionality reduction is one of the basic operations in the toolbox of data-analysts and designers of machine learning and pattern recognition systems. Given a large set of measured variables but few observations, an obvious idea is to reduce the degrees of freedom in the measurements by representing them with a smaller set of more “condensed” variables. Another reason for reducing the dimen...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Data Mining and Knowledge Discovery
سال: 2012
ISSN: 1384-5810,1573-756X
DOI: 10.1007/s10618-012-0268-8