1. What is similarity?
Similarity refers to the degree of similarity or identity between two or more things. In computer science, similarity is often determined by comparing attributes, characteristics, or metrics between two objects. This can help us identify similar or related data and perform tasks such as classification, clustering, search and recommendation. For example, in image recognition, the similarity between two images can be calculated by comparing their pixels, shapes, and colors to determine whether they belong to the same object. In natural language processing, the similarity of words, phrases, and grammatical structures between two pieces of text can be calculated to perform tasks such as text matching, information retrieval, and semantic analysis.
2. Several similarity measurement methods
2.1 Euclidean distance
Euclidean distance calculates similarity based on the distance between the position coordinates of two points in Euclidean space. It is suitable for data based on continuous variables such asImage and audio processingand other fields. The smaller the value of the Euclidean distance, the more similar the two points are.
2.2 Cosine similarity
Cosine similarity calculates similarity based on the angle between two vectors. It is suitable for data based on discrete variables, such asText CategorizationandRecommended systemand other fields. The larger the value of cosine similarity, the more similar the two vectors are.
2.3 Jaccard similarity coefficient
The Jaccard similarity coefficient calculates the similarity based on the size of the intersection and union between two sets. It works well for data based on binary variables likeText Categorizationandnetwork analysisand other fields. The larger the value of Jaccard similarity coefficient, the more similar the two sets are.
2.4 Edit distance
Edit distance calculates the similarity based on the number of operations between two strings. It is suitable for tasks based on text data such aslanguage translationandSpeech Recognitionand other fields. The smaller the edit distance value, the more similar the two strings are.
2.5 Pearson correlation coefficient
The Pearson correlation coefficient is a measure used to calculate the degree of linear correlation between two continuous variables. Its value range is between -1 and 1. The closer the value is to 1, the two variables are positively correlated, and the closer the value is to -1, the two variables are negatively correlated.
2.6 Manhattan distance
Manhattan distance is a measure used to calculate the distance between two vectors. It refers to the sum of the absolute values of the differences between the two vectors in each dimension. it applies toImage ProcessingandLogistics fieldScenarios where the actual distance traveled between two points needs to be calculated.
2.7 Hamming distance
Hamming distance is a measure used to calculate the degree of difference between two binary sequences. It refers to the number of different values in two sequences at the same position, often used inData compression and encodingand other fields.
2.8 Tag similarity
Label similarity is a measure used to calculate the similarity between two sets, used to calculate the similarity of different objects or entities, such asMovie recommendationsand image classification.
