https://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf

University of Michigan - Ann Arbor

T-SNE is an unsupervised machine learning method that is used to visualize the higher dimensional data in low dimensions. T-SNE is used for designing/implementation and can bring down any number of feature space into 2-D feature space.

T-Distributed Stochastic Neighbour Embedding (T-SNE)

T-SNE gives the impression that it has classified the data by bringing it to two-dimensions but in reality, it doesn’t reduce the dimensions. It is a visualizer, which tells how each class is distributed and is there any overlap between them. In hyper-dimensional space, the euclidean distance becomes useless. The similarity between the classes in higher dimensions corresponds to the short distance between them in low dimensions. ( the data points of each class becomes very close) .
concept

Idea about T-SNE

Visualizing Data using t-SNE

The algorithm starts by calculating the probability of similarity of points in high-dimensional space and calculating the probability of similarity of points in the corresponding low-dimensional space. The similarity of points is calculated as the conditional probability that a point A would choose point B as its neighbor if neighbors were picked in proportion to their probability density under a Gaussian (normal distribution) centered at A. It uses T-test from the T-distribution. It then tries to minimize the difference between these conditional probabilities (or similarities) in higher-dimensional and lower-dimensional space for a perfect representation of data points in lower-dimensional space. To measure the minimization of the sum of the difference of conditional probability t-SNE minimizes the sum of Kullback-Leibler divergence of overall data points using a gradient descent method.

Overview of working of T-SNE

Both PCA and LDA are used for visualization and dimensionality reduction but T-SNE is specifically used for visualization purposes only. It is well suited for the visualization of high-dimensional datasets. Unlike PCA and LDA, T-SNE is a non-linear data visualizer. It means it doesn’t form a linear line to separate the classes or to calculate the variance and it doesn’t use any norm or distance metric to calculate the distance between points.

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