Explain cosine similarity and its applications

Cosine similarity is a measure of similarity between vectors. Mathematically, it is defined as the dot product of two vectors divided by the product of their Euclidean norms. In the field of machine learning, cosine similarity is widely used in tasks such as text classification and recommendation systems to compare the similarity between two vectors. Cosine similarity is calculated by calculating the cosine of the angle between two vectors. Note that these vectors are usually non-zero and exist in inner product space.

This article will introduce what is cosine similarity? and the role of cosine similarity.

What is Cosine Similarity

Cosine similarity is a commonly used similarity measurement technique that can be found in multiple libraries and tools, such as Matlab, SciKit-Learn, and TensorFlow. It is a value bounded by 0 and 1. Cosine similarity is used to measure the cosine value of the angle between two non-zero vectors A and B, thereby measuring the similarity between them.

Assuming that the angle between two vectors is 90 degrees, cosine similarity is a value bounded by 0 and 1. When the value is closer to 0, it means that the two vectors are orthogonal or perpendicular to each other, indicating that the similarity between them is low. When the cosine similarity is closer to 1, it means that the angle is smaller and the images are more similar.

As the cosine similarity measure gets closer to 1, the angle between the two vectors A and B gets smaller.

The role of cosine similarity

In the fields of computer vision and data mining, vector similarity measures in high-dimensional spaces are widely used.

1. Document similarity

Using cosine similarity to measure the similarity between documents is a common use case.

In order to quantify the similarity between two documents, we need to convert the words or phrases in the document or sentence into vector representations. By using a vector representation of a document, we can apply the cosine similarity formula to calculate similarity. This makes it possible to quantify the similarity between two documents.

In the above scenario, a cosine similarity of 1 means that the two documents are completely similar, while a cosine similarity of 0 indicates that there is no similarity between the two documents.

2. Pose Matching

Pose matching involves comparing poses containing key points of joint positions.

Pose estimation is a computer vision task that is usually solved using deep learning methods, such as convolutional pose machines, stacked hourglass, and PoseNet, etc.

Pose estimation is the process of deriving the positions and orientations of important body parts and joints from an image or image sequence.

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Cosine similarity can also be used in other application areas, such as recommendation systems, plagiarism detectors and data mining. It can even be used as a loss function when training neural networks. The logic behind cosine similarity is easy to understand and can be implemented in most modern programming languages.

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