Reducing Dimensionality

Antoine Guillot explains some of the basic concepts of variable reduction in a data analysis:

Each of these people can be represented as points in a 3 Dimensional space. With a gross approximation, each people is in a 50*50*200 (cm) cube. If we use a resolution of 1cm and three color channels, then can be represented by 1,000,000 variables.
On the other hand, the shadow is only in 2 dimensions and in black and white, so each shadow only needs 50*200=10,000 variables.
The number of variables was divided by 100 ! And if your goal is to detect human vs cat, or even men vs women, the data from the shadow may be enough.

Read on for intuitive discussions of techniques like principal component analysis and linear discriminant analysis.  H/T R-Bloggers

Related Posts

Using Convolutional Neural Networks To Recognize Features In Images

Michael Grogan shows how you can use Keras to perform image recognition with a convolutional neural network: VGG16 is a built-in neural network in Keras that is pre-trained for image recognition. Technically, it is possible to gather training and test data independently to build the classifier. However, this would necessitate at least 1,000 images, with […]

Read More

Combining Stream Analytics And Azure ML With Power BI

Brad Llewellyn shows us how to feed Azure ML predictions into Power BI via Azure Stream Analytics: Today, we’re going to talk about combining Stream Analytics with Azure Machine Learning Studio within Power BI.  If you haven’t read the earlier posts in this series, Introduction, Getting Started with R Scripts, Clustering, Time Series Decomposition, Forecasting, Correlations, Custom R Visuals, R Scripts in Query […]

Read More

Categories

August 2017
MTWTFSS
« Jul Sep »
 123456
78910111213
14151617181920
21222324252627
28293031