Dimensionality Reduction - Machine Learning
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# Dimensionality Reduction - Machine Learning

In Machine Learning or Statistics, dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. This can be achieved using two processes: Feature Selection and Feature Projection

### Feature Selection

• Feature selection methods try to find a subset of the original variables by the following strategies:
• the Filter strategy (e.g. information gain)
• the wrapper strategy (e.g. search guided accuracy)
• the embedded strategy (features are selected to add or be removed while building the model based on the prediction scores)

### Feature Projection

• In this post, we are more focused on approaches that help with feature extraction for high-dimensional data. Feature projection transforms data in the high-dimensional space to a space of fewer dimensions. The transformation may be linear or nonlinear depending on the approach we take and the type of data we have on hand.

### Feature Projection Techniques

• Principal Component Analysis (PCA)

• PCA takes a dataset with a lot of dimensions and flattens it to 2 or 3 dimensions so that we can take a look at it
• It tries to find a meaningful way to flatten the data by focusing on the things that are different among the variables (or features)
• PCA looks at the variables/features with the most variation
• The first principal component (or axis) lies in the direction where the variation in the dataset is the maximum.
• The second PC is in the direction of the second most variation axis
• and so on…
• The number of dimensions of the dataset is equal to the number of PCs after the dataset has been projected from higher dimension to lower dimension

• Formulation of Principal Component
1. A random axis (passing through origin) is drawn at first in the sample space and each point in the dataset is then projected to the axis.
2. The distance of each projected point from the origin is calculated and their sum of squares is calculated
3. This is done for every possible axis passing through origin
4. The one with the highest square sum gives the 1st PC (as it is the one along the direction with maximum variation in the dataset)
5. Similarly, the second largest sum for the axis is taken as the 2nd PC and so on…
• NOTE: The PCs are orthogonal to one another.
• Implementing PCA on a 2-D dataset
1. Normalize the data
• Done by subtracting the respective means from the numbers for each feature
• This produces a dataset whose mean is zero
2. Covariance Matrix
• Compute the covariance matrix for the dataset
1 Matrix (Covariance) = $$\begin{bmatrix}Var[X_2] & Cov[X_1, X_2]\\Cov[X_2, X_1] & Var[X_2]\end{bmatrix}$$ 
3. Eigenvalues and Eigenvectors calculation
• Calculate the eigen values and vectors for the above calculated covariance matrix
• $\lambda$ can be defined as the eigen value of a matrix A if if satisfies the following characteristic equation
1 det($$\lambda$$I - A) = 0 
• Also, for each eigen value $\lambda$, there exists a corresponding eigen vector v such that
1 ($$\lambda$$I - A)v = 0 
4. Forming a feature vector
• Order the obtained eigenvalues from largest to smallest so that it sorts in the order of its significance
• If we have a dataset with n variables (or features), then we will have n number of eigenvalues and eigenvectors
• To reduce the dimensions of the dataset, just select the first p eigenvalues and ignore the rest.
• Now, we form a feature vector which is a matrix of the eigenvectors as shown below
1 Feature Vector = ($$eig_1, eig_2, eig_3, ...$$) 
5. Forming Principal Components
• We now form our principal components using the above calculated figures
1 NewData = $$FeatureVector^T * ScaledData^T$$ 
• So,
• NewData is the matrix consisting of the principal components
• FeatureVector is the matrix containing the eigenvectors
• ScaledData is the scaled version of original dataset
• To implement PCA with Python checkout this code here!
• Linear Discriminant Analysis (LDA)

• LDA is like PCA, but it focuses on maximizing the separability among known categories
• LDA tries to maximize the separation of known categories
This post is written by Ashish Jaiswal