Submitted by TKMater t3_10y08vt in MachineLearning
how to calculate similarity between two vectors? I want a similarity metric that take into accounts both the directions and magnitudes of vectors.
Comments
TKMater OP t1_j7vayeo wrote
I have classified some data points as anomaly in timeseries data and calculated feature importance vectors for them. Now I want to calculate similarity between two anomalous data points based on their feature importance vector.
BrohammerOK t1_j7x21pg wrote
If you care as you said about both magnitude and direction, try with L2 (Euclidean distance) , not cosine similarity.
eamonnkeogh t1_j7xw5xp wrote
If you settle on the Euclidean distance (or weighted Euclidean distance) and you need to do it FAST, can I recommend the amazing MASS algorithm https://www.cs.unm.edu/~mueen/FastestSimilaritySearch.html
RitsusHusband t1_j7v7crt wrote
Could you take the squared difference?
Jucuco t1_j7vsc6p wrote
Inner product between metrics tells you the projection of one onto the other. Whatever the space you're building reperesents
lonelyrascal t1_j7vw6f4 wrote
Try cosine similarly?
lonelyrascal t1_j7vwf3f wrote
Well it also depends on whether you have sparse vectors or not... Some metrics work best for sparse vectors and other work for dense vectors. Check out scikit learn's pairwise distances metrics to understand better
Maleficent-Sea-2015 t1_j7x34tl wrote
You can use Cosine similarity
BenoitParis t1_j7v9wue wrote
Lots to choose from:
https://docs.scipy.org/doc/scipy/reference/spatial.distance.html
How do your vectors look like? What do you intend to do with them? Will you be clustering them? Indexing them? How many are there? How did you obtain them? What do they represent? What is their type?