Curse of Dimensionality may Be Reduced With Collecting Accurate and Precise Data
More data features may lead to complications in datasets in feature dimensional space
Shikha SaxenaSep 9 · 2 min read
Clean, comprehensive data and also well integrated one, also contain too many features and dimensions to be comprehended with ease and modeled efficiently. Hence the phrase Curse of Dimensionality! Curse of Dimensionality may be reduced with collecting accurate and precise data which can be handled easily and may be applied to different types of models with ease. Precise and accurate datasets which are fed into models, as machine learning training data sets, should be simple to fit all models easily.
Input Variables
Independent variables are the input value or input variables of a given function. These are independent values in an experiment and can be controlled. If data is represented as in spreadsheet the columns are the input variables represented as dimensions, that are fed in model and rows constitute the points in a n-dimensional feature space. This is a depiction of geometric representation of a dataset.
Curse of Dimensionality
Large dimension in this vast feature space means that numerous input variables in that space and small number of points or rows of data in comparison and hence a miniscule non represented or less meaningful dataset sample.
These high dimensions may not be as useful for the predictions made in that space. This can impact the performance of machine learning algorithms fit on data. This is called Curse of Dimensionality!
Dimensionality Reduction
Therefore it is important to reduce the number of input features hence reducing the dimensions of the featured space. This is called Dimensionality Reduction. Fewer input variables mean lesser dimensions and simpler dataset structure, in machine learning model, also referred to as Degrees of Freedom.
A model with more degrees of freedom may not fit or overfit the training dataset. This model will not perform well with new set of data.
Small and simpler dimensions with few input variables will create meaningful data sets with essence that will give required results and solve the purpose.
How to Beat the Curse of Dimensionality?
Too complex and multi-dimensional dataset, with higher degrees of freedom will be too complicated to handle and low dimensional datasets with fewer inputs may not solve the purpose of predictive analysis.
The only way to beat the curse of Dimensionality is to increase research and knowledge to prepare precise and accurate datasets prior to modeling. This might be performed through vigorous data cleaning and data scaling techniques in future.CodeX
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Shikha Saxena
A Technical Writer, an artist and blogger by choice. Passionate about reading , writing and editing. http://www.shikhasaxena.com and https://www.dnabox.co/
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