Techniques For High Dimension Data Analysis

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.

Let’s look at few examples of high-dimensional data.

Common Examples Of High-Dimensional Data

• Healthcare data: Healthcare datasets are a typical example of high-dimensional data. It is very common to have more features than observations in these datasets. Here, the number of individual data can be massive, including weight, blood pressure, immune system status, resting heart rate, height, existing conditions, surgery history, etc.
• Financial data : High-dimensional data is also widespread in financial datasets as the number of features can be pretty large. In these datasets, the features like market cap, trading volume, PE ratio, dividend rate, etc. are much larger than the individual stocks.
• Genomics: It is another area where high-dimensional data is found. Here the number of gene features of an individual can be massive.

High-dimension data analysis is at the heart of modern data analysis. Many techniques can be used in the reduction of high-dimensional data. You can master these techniques by enrolling in a good data science training course. Some of them are listed below.

Techniques For High Dimension Data Analysis

Missing Values Ratio: Data columns with missing values don’t 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.

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 attribute’s 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.

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://datasciencetrainingusa.wordpress.com/2021/08/04/techniques-for-high-dimension-data-analysis/