Applying TruncatedSVD for Dimensionality Reduction in NLP
Introduction
Welcome to this lesson where we’ll delve into a critical aspect of text data analysis: Dimensionality Reduction. As you have learned in the previous lessons, the raw text is transformed into a feature matrix using techniques like Bag-of-Words and TF-IDF representation. But these matrices are often high-dimensional, which increases the complexity of the model, causes longer training times, and can even degrade model performance due to the so-called “curse of dimensionality”. To address these issues, we resort to Dimensionality Reduction techniques which reduce the size of the feature space. By the end of this lesson, you will have learned the basics of Dimensionality Reduction and how to implement the TruncatedSVD method in Python on the IMDB movie reviews dataset.
Understanding Dimensionality Reduction
In many machine learning problems, data is represented using a large number of features or dimensions. When the dimensionality or number of features is too large, data can become sparse and scattered, potentially making the learning algorithm perform poorly, struggle to find patterns, or even overfit to the training set noise.
This is a well-known problem called "curse of dimensionality" and to tackle it, we use a set of techniques known as Dimensionality Reduction. The goal of dimensionality reduction is to reduce the number of features in your data while retaining the essential information and structure. The transformed data is a new representation of the original data but in a reduced feature space. It generally results in lesser computational requirements, lower storage space, and perhaps most importantly, improved performance by reducing overfitting.
One popular method for dimensionality reduction is Singular Value Decomposition (SVD). In NLP, we often use its variant called TruncatedSVD, which reduces the feature space to a user-specified smaller dimension, while preserving maximum data variance.
Implementing TruncatedSVD in Python
The dimensionality reduction technique we'll be using in this lesson is TruncatedSVD, which is available in the sklearn.decomposition module of the scikit-learn library. We can instantiate a TruncatedSVD object and specify the number of desired output features in the n_components parameter.
In the above code, n_components=50 specifies that we want to reduce our feature space to 50 dimensions. The fit_transform method is then used to apply this transformation to a given matrix.
