🌱Vectors for classification

Defining Multi-Classification

Classification refers to when a model is used to predict 2 or multiple labels (more than 2). As an example, this could be when given an animal image, the individual is required to label the category that the animal belongs to from a list of pre-defined categories.

Traditional Classification

Traditional Multi-classification

In the classification example above, the image is read and fed through a neural network. The neural network, trained on predicting whether an image is a dog/cat/rabbit/emu is then given a probability that it can belong in each class.

Vectors reframe traditional classification into a vector search problem

Let us reframe our example of image classification (labeling an image based on the given captions to identify the best category) using vector search. Instead of predicting the most likely label using a neural network (which is how it was previously done), the labels were, instead, encoded using a deep learning model. The images were also encoded and then a vector search was performed on the projections of these encodings to identify the most similar images to the labels.

Search Approach To Multi-Classification

Let us now quickly compare the advantages and disadvantages of each approach.

Advantages and Disadvantages of Vector Similarity Approach

There are several advantages to this approach:

• Resolves the cold-start issue (in traditional approaches, classification neural networks would have to be re-trained in order to adapt to new categories)

• Reduced cost of data science experiments - using excellent out-of-the-box vectors/similarity search that resolves this issue means you can then reduce the cost of initial data science experiments and bring data to value quickly

There are also several disadvantages to this approach:

• If you require vectors to fit well on pre-defined, it requires more data science expertise to finetune these vectors compared to traditional multi-classification approaches.