Techniques For High Dimension Data Analysis

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Techniques For High Dimension Data Analysis
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High-dimensional data is a dataset where the
number of features is larger than the number of
observations. In a dataset, there can be any
number of features. But this data can only be
considered high-dimensional if the number of
observations or independent size is less than the
features. High-dimensional data poses many
challenges for data analysis as it makes the
calculation difficult. Also, with such data, it
is challenging to have a deterministic result.
This means that it is nearly impossible to find a
model that can describe the relationship between
the response and the predictor variable. It
happens due to a lack of observations or sample
size to train the model on. Lets look at few
examples of high-dimensional data.
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Common Examples Of High-Dimensional Data
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• Missing Values Ratio  Data columns with missing
  values dont have much useful information. So,
  you can remove the columns with missing values
  exceeding the given threshold.
• Low Variance Filter You can remove the data
  columns containing variance lower than the given
  threshold. But normalization is needed before
  using this technique as the variance is range
  dependent.
• High Correlation Filter Reduce the pairs of
  columns to one with a correlation coefficient
  higher than the given threshold. But as
  correlation is scale-sensitive, column
  normalization is necessary for correlation
  comparison.

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• Principal Component Analysis (PCA)- It is a
  statistical procedure that changes the original n
  coordinates into a new set of n coordinates of a
  dataset called principal components. The first
  step to PCA is the standardization of data.
• Random Forests / Ensemble Trees This technique
  is extremely useful in feature selection as an
  effective classifier. One way of dimension
  reduction is to generate a carefully constructed
  large set of trees against a target attribute and
  then find the most informative subset of features
  through each attributes usage statistics
  relative to other attributes usage.
• You can also generate a large set of shallow
  trees, with each tree trained on a small fraction
  of the number of attributes. The attribute
  selected as the best split is the most
  informative feature to retain.

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Besides these, you can also use the Backward
feature elimination and forward feature
construction technique. However, backward feature
elimination and forward feature construction are
time-consuming and computationally expensive. So,
these are applied to data sets with a relatively
low number of input columns. Conclusion You can
acquire high-dimensional data reduction
techniques during data science training in
California. Each of the given methods is useful
in effectively reducing high-dimension data.
Dimensionality reduction not only speeds up
algorithm execution but improves model
performance. Source https//datasciencetrainingu
sa.wordpress.com/2021/08/04/techniques-for-high-di
mension-data-analysis/
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