For the problem I'm working on, finding distances between two sequences to determine their similarity, sequence order is very important. However, the sequences that I have are not all the same length, so I pad any deficient strings with empty points such that both sequences are the same length in order to satisfy the Hamming distance requirement. Is there any major problem with me doing this, since all I care about are the number of transpositions (not insertions or deletions like Levenshtein does)?
I've found that Hamming distance is much, much faster than Levenshtein as a distance metric for sequences of longer length. When should one use Levenshtein distance (or derivatives of Levenshtein distance) instead of the much cheaper Hamming distance? Hamming distance can be considered the upper bound for possible Levenshtein distances between two sequences, so if I am comparing the two sequences for a order-biased similarity metric rather than the absolute minimal number of moves to match the sequences, there isn't an apparent reason for me to choose Levenshtein over Hamming as a metric, is there?
Different definitions of an edit distance use different sets of string operations. Levenshtein distance operations are the removal, insertion, or substitution of a character in the string. Being the most common metric, the term Levenshtein distance is often used interchangeably with edit distance.
The Levenshtein distance used as a metric provides a boost to accuracy of an NLP model by verifying each named entity in the entry. The vector search solution does a good job, and finds the most similar entry as defined by the vectorization.
The Levenshtein distance is a string metric for measuring difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (i.e. insertions, deletions or substitutions) required to change one word into the other.
The Levenshtein distance is a similarity measure between words. Given two words, the distance measures the number of edits needed to transform one word into another. There are three techniques that can be used for editing: Insertion.
That question really depends on the types of sequences you are matching, and what result you want.
If it's not a problem that "1234567890" and "0123456789" are considered totally different, indeed Hamming distance is fine.
In addition to the right Johan answer, the padding can be problematic.
For example, when you compare 123
to 123456
it's different if you pad either at the end of the string or at the start of the string. The similarity of ___123
with 123456
is 0, but The similarity of 123___
with 123456
is 3.
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