Comments (4)
garydoranjr
commented on August 23, 2024
I've seen this error before if there are duplicate instances in the dataset. This can cause the matrix in the optimization program to be rank-deficient.
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gnawJ
commented on August 23, 2024
Thanks for the reply!
Is it possible to turn off the optimization? Thx.
Also, I checked my dataset, it seems like there are no duplicated instances.
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garydoranjr
commented on August 23, 2024
No, the optimization cannot be turned off; it is fundamental to SVM classification: https://en.wikipedia.org/wiki/Support_vector_machine#Kernel_trick
It looks like the SVM code is being called by mis_stress.py (not in this repository). It is possible that however the data is manipulated by that code prior to calling the SIL fit method is leading to this issue.
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yuchun1214
commented on August 23, 2024
I have encountered the same issue in my implementation of SVM. I solve the problem by standardizing the data.
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Related Issues (17)
- Example Code not Working HOT 1
- the example does not run on my machine HOT 5
- "get_params" of miSVM HOT 1
- Multi label implement HOT 2
- MissSVM result HOT 3
- same sign for all instance-level prediction HOT 5
- Different results in python 2.7.8 and python 3.6.8 HOT 1
- Overflow- INT_MAX reached HOT 2
- Class weights HOT 1
- Prediction scores instead of labels HOT 3
- NaN predicted values HOT 2
- Getting a warning for numerical instability and algorithm is unable to optimize
- Help -- ValueError : Rank(A) < p or Rank([P; A; G]) < n
- Attribute Error running example code HOT 2
- memory error on medium scale HOT 1
- Instance level predictions HOT 5
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