How can you clean data that includes multiple modalities?

1 Identify the modalities

The first step is to identify the modalities that are present in your data and how they are related to each other. For example, you may have a dataset of product reviews that contains text, ratings, images, and metadata. You need to know how each modality is structured, stored, and labeled, and how they are aligned or synchronized. This will help you to decide what kind of cleaning tasks you need to perform on each modality and how to combine them later.

2 Handle missing data

Missing data is a common problem in multimodal data, as some modalities may be unavailable, incomplete, or corrupted for some instances. For example, some product reviews may have no images or ratings, or some images may be broken or blurry. You need to handle missing data in a way that does not compromise the quality or integrity of your data. You can either remove the instances with missing data, impute the missing values with some reasonable estimates, or use techniques that can handle missing data, such as masked learning or generative models.

3 Normalize and standardize data

Different modalities may have different scales, ranges, and distributions that can affect the performance of your machine learning models. For example, text data may have different lengths, languages, and vocabularies, while image data may have different resolutions, colors, and brightness. You need to normalize and standardize your data to make them comparable and compatible across modalities. You can use techniques such as tokenization, stemming, lemmatization, stopword removal, and encoding for text data, and resizing, cropping, grayscale conversion, and histogram equalization for image data.

4 Detect and remove outliers

Outliers are data points that deviate significantly from the rest of the data and may indicate errors, anomalies, or noise. For example, some product reviews may have extremely high or low ratings, or some images may have irrelevant or inappropriate content. You need to detect and remove outliers to improve the accuracy and robustness of your machine learning models. You can use techniques such as descriptive statistics, box plots, z-scores, or clustering for numerical data, and natural language processing, computer vision, or domain knowledge for non-numerical data.

5 Reduce dimensionality

Dimensionality reduction is the process of reducing the number of features or variables in your data while preserving the most relevant information. For example, you may have a dataset of product reviews that contains thousands of words, ratings, images, and metadata. You need to reduce dimensionality to speed up the computation, avoid overfitting, and enhance the interpretability of your machine learning models. You can use techniques such as feature selection, feature extraction, or feature fusion for different modalities.