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A brute-force method is simply to try all rotation angles and decide if 2 images are a rotation of one another. However, there are features (eg fourier coefficients) which are rotation-invariant. So comparing these rotation-invariant features is a similarity metric for determining if 2 images are a rotation of one another. References: Rotation Invariance in ...


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When you have some historical data on good/bad matches (and "okay" features to describe these matches), you can try a Siamese Neural Network. This type of model is a "few shot" model, meaning that it is designed to work with a relatively small ammount of (training) data and potentially noisy features. Essentially you fit a model to pairs ...


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Take for example the Euclidean distance L2, defined by: $$L_2(x,y) = \left(\sum_{i=1}^{d} (x_i-y_i)^2\right)^{1/2}$$ where $d$ is the vector dimension. You can easily add a term $\alpha \in (0,1)$ and put more weight on the first term, for example: $$L_2(x,y) = \left(\sum_{i=1}^{d} (\alpha x_i-(1-\alpha)y_i)^2\right)^{1/2}$$ That will certainly be non-...


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One method is Kolmogorov-Smirnov test. Kolmogorov-Smirnov test checks whether two samples are drawn from the same continuous distribution where sample sizes can be different. It's p-value is close to 0 when two samples follow the same distribution and close to 1 when they do not follow the same distribution. So you can use 1 - (p-value) as a similarity ...


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As you mention it might be solved via clustering, but given you need the top n to each other you can go as follows: Assuming you have matrix X of nxm (n- batteries m- features/attributes of each one) Define a distance metric (Euclidean, Mahalanobis, etc) Calculate the distance between a battery j and all the other batteries i - j Sort the top n distances ...


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That is often called nearest-neighbor search. The most common methods require a distance metric. Given the features of battery pack, how close to each other are they?


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