Dimension reduction | data science

Dimensionality reduction is a common technique used in data science to reduce the number of features in a data set while preserving its important characteristics.
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This can help simplify the data set and make it easier to analyze and visualize.
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Here are some popular dimension reduction methods and techniques, along with a real time example.
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Principal component analysis PCA.
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PCA is a commonly used technique for dimensionality reduction.
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It works by finding the directions of maximum variance in the data and projecting the data onto a smaller number of dimensions.
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For example, if we have a data set with 10 features, PCA can be used to reduce it to two or three principal components that explain the most variance in the data.
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Real time example.
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PCA can be used in image processing to reduce the dimensionality of high resolution images while preserving important features such as edges and textures.
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He distributed Stochastic Neighbor Embedding T sne.
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Tsang is a powerful technique for visualizing high dimensional data.
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It works by reducing the dimensionality of the data while preserving the pairwise distances between points in the original high demensional space.
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Real time example.
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Tsang can be used to visualize the relationship between different types of cancer based on gene expression data.
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Linear Discriminate Analysis LDA.
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LDA is a supervised learning technique that can be used for both classification and dimensionality reduction.
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It works by finding the directions that maximize the separation between classes while minimizing the variance within each class.
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Real time example.
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LDA can be used in speech recognition to reduce the dimensionality of the speech features while preserving the information that is important for distinguishing different words.
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Nonnegative matrix factorization, NMF.
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NF is a matrix factorization technique that can be used for dimensionality reduction and feature extraction.
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It works by decomposing a high dimensional data matrix into two lower dimensional matrices.
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Real time example.
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NMF can be used in topic modeling to extract the most important topics from a large corpus of documents.
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These dimensionality reduction techniques can help data scientists handle high dimensional data sets and extract important information from them in a more efficient and effective manner.