A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces

2006 ◽  
pp. 149-156
Author(s):  
Motoaki Kawanabe ◽  
Gilles Blanchard ◽  
Masashi Sugiyama ◽  
Vladimir Spokoiny ◽  
Klaus-Robert Müller
Keyword(s):  
Dimension Reduction ◽  
Reduction Procedure ◽  
Non Gaussian

scholarly journals Burning the Haystack to Save the Needle: Dimension Reduction and Privacy in Text and Network Data

2019 ◽  
Author(s):  
Alexander Murray-Watters
Keyword(s):  
Social Networks ◽  
Dimension Reduction ◽  
Predictive Modeling ◽  
Reduction Technique ◽  
Distance Measures ◽  
Network Data ◽  
Data Types ◽  
Reduction Procedure ◽  
Anonymized Data

This poster presents results from applying a new dimension reduction technique (UMAP) to a wide variety of data types, ranging from online text to social networks, for the purpose of creating useful, but anonymized, data. As the dimension reduction procedure produces meaningful distances and supports arbitrary distance measures, it can be applied to a variety of problems, and produces data that is useful for both visualization and predictive modeling. Included is a description of the dimension reduction procedure, the results of its application, and a discussion of planned future use.

Dimension Reduction for Non-Gaussian Data by Adaptive Discriminative Analysis

2019 ◽  
Vol 49 (3) ◽  
pp. 933-946 ◽  
Author(s):  
Tingjin Luo ◽  
Chenping Hou ◽  
Feiping Nie ◽  
Dongyun Yi
Keyword(s):  
Dimension Reduction ◽  
Discriminative Analysis ◽  
Non Gaussian ◽  
Gaussian Data

scholarly journals Dimension Reduction via Penalized GLMs for Non-Gaussian Response: Application to Stock Market Volatility

2021 ◽  
Vol 14 (12) ◽  
pp. 583
Author(s):  
Tao Li ◽  
Anthony F. Desmond ◽  
Thanasis Stengos
Keyword(s):  
Stock Market ◽  
Dimension Reduction ◽  
Market Volatility ◽  
Corporate Bond ◽  
Stock Market Volatility ◽  
Reduction Techniques ◽  
Bond Yield ◽  
Macroeconomic Indicators ◽  
Non Gaussian ◽  
Box Cox Transformation

We fit U.S. stock market volatilities on macroeconomic and financial market indicators and some industry level financial ratios. Stock market volatility is non-Gaussian distributed. It can be approximated by an inverse Gaussian (IG) distribution or it can be transformed by Box–Cox transformation to a Gaussian distribution. Hence, we used a Box–Cox transformed Gaussian LASSO model and an IG GLM LASSO model as dimension reduction techniques and we attempted to identify some common indicators to help us forecast stock market volatility. Via simulation, we validated the use of four models, i.e., a univariate Box–Cox transformation Gaussian LASSO model, a three-phase iterative grid search Box–Cox transformation Gaussian LASSO model, and both canonical link and optimal link IG GLM LASSO models. The latter two models assume an approximately IG distributed response. Using these four models in an empirical study, we identified three macroeconomic indicators that could help us forecast stock market volatility. These are the credit spread between the U.S. Aaa corporate bond yield and the 10-year treasury yield, the total outstanding non-revolving consumer credit, and the total outstanding non-financial corporate bonds.

scholarly journals Order-Preserving Dimension Reduction Procedure for the Dominance of Two Mean Curves with Application to Tidal Volume Curves

Biometrics ◽  
2008 ◽  
Vol 64 (3) ◽  
pp. 931-939 ◽  
Author(s):  
Sang Han Lee ◽  
Johan Lim ◽  
Marina Vannucci ◽  
Eva Petkova ◽  
Maurice Preter ◽  
...  
Keyword(s):  
Tidal Volume ◽  
Dimension Reduction ◽  
Reduction Procedure
Sign in / Sign up

Export Citation Format

Share Document