Based on the provided information and general machine learning principles, the correctly matched ML paradigms for the given use cases are as follows:

Here's the breakdown with reasoning:

1. Fraud detection: Binary classification

Reason: Fraud detection typically involves classifying transactions as either fraudulent or not fraudulent. This is a two-class problem, making binary classification the appropriate paradigm. We provide labeled data of fraudulent and non-fraudulent transactions to train the model.

Why not the others: Multi-class classification would be used if we had more than two categories (e.g., low risk, medium risk, high risk fraud). K-means clustering and dimensionality reduction are unsupervised techniques and are less suitable for a classification task where labeled data is available.

2. Customer segmentation: K-means clustering

Reason: Customer segmentation aims to group customers based on their similarities without pre-defined labels. K-means clustering is an unsupervised learning algorithm perfect for this as it groups data points into clusters based on distance from cluster centroids.

Why not the others: Binary and multi-class classification needs the labels for the datasets whereas customer segmentation's target is to find label insights based on similarity. Dimensionality reduction could be a PRE process for K-means to reduce the number of features.

3. Image classification: Multi-class classification

Reason: Image classification generally involves categorizing images into multiple distinct classes (e.g., cat, dog, bird). Multi-class classification is designed for problems with more than two classes.

Why not the others: binary classification needs only two classes in the dataset. K-means clustering and dimensionality reduction are unsupervised learning techniques; image classification normally needs labeled data to train the model.

4. Feature selection: Dimensionality reduction

Reason: Feature selection aims to reduce the number of features used in a machine learning model, aiming to remove noisy or non-predictive features to improve model performance. Dimensionality reduction techniques are well-suited for feature selection, as they reduce the number of features while preserving important information.

Why not the others: Classification methods don't reduce the feature dimensions; clustering needs to define the clusters, which doesn't align with feature selection purposes directly.

Therefore, the AI-recommended answer aligns with the consensus that: Binary and Multi-class classifications are types of supervised learning (because they require labeled data) and K-means clustering and Dimensionality reduction are types of unsupervised learning (because they do not need labels).

Scikit-learn - K-Means Clustering
Scikit-learn - Dimensionality Reduction
IBM - Classification in Machine Learning
TensorFlow - Image classification