Hello! Yes, you can use Convolutional Neural Networks (CNNs) for classification of feature vectors. CNNs are commonly used for image classification tasks, where the input data is in the form of images.
If you have already extracted features directly from the data and have feature vectors as input, you can still use CNNs for classification. However, since CNNs are designed to work with image data, you may need to reshape your feature vectors to have a 2D structure that resembles an image.
One approach is to reshape your feature vectors into a 2D matrix, where each row represents a feature vector. You can then treat this reshaped data as a grayscale image. Alternatively, if your feature vectors have a specific order or sequence, you can reshape them into a 2D matrix where each row represents a time step or sequence element.
Once you have reshaped your feature vectors into a suitable 2D structure, you can use CNN architectures like those used for image classification, such as the popular LeNet, AlexNet, VGG, or ResNet. These architectures consist of convolutional layers, pooling layers, and fully connected layers, which can learn hierarchical representations and perform classification tasks.
I hope this helps you get started with using CNNs for classification of feature vectors.
