Performs a binary search to get affinities in such a way that each conditional Gaussian has the same perplexity.
Performs a binary search to get affinities in such a way that each conditional Gaussian has the same perplexity.
The input dissimilarity vector which represents the current vector distance to the other vectors in the data set
The log of the perplexity, which represents the probability of having affinity with another vector.
The maximum iterations to limit the computational time.
The allowed tolerance to sacrifice precision for decreased computational time.
The lower bound of beta
The upper bound of beta
The current iteration
Returns the affinity vector of the input vector.
Approximate the affinity by fitting a Gaussian-like function
Approximate the affinity by fitting a Gaussian-like function
The dissimilarity vectors which represents the distance to the other vectors in the data set.
The user defined parameters of the algorithm
Returns new set of BreezeLabeledVector with dissimilarity vector
Normalizes the input vectors so each row sums up to one.
Normalizes the input vectors so each row sums up to one.
The affinity vectors which is the quantification of the relationship between the original vectors.
Returns new set of BreezeLabeledVector with represents the binding probabilities, which is in fact the affinity where each row sums up to one.
Compute pair-wise distance from each vector, to all other vectors.
Compute pair-wise distance from each vector, to all other vectors.
The input vectors, will compare the vector to all other vectors based on an distance method.
Returns new set of BreezeLabeledVector with dissimilarity vector
Compute the final outlier probability by taking the product of the column.
Compute the final outlier probability by taking the product of the column.
The binding probability vectors where the binding probability is based on the affinity and represents the probability of a vector binding with another vector.
Returns a single double which represents the final outlierness of the input vector.
TransformDataSetOperation applies the stochastic outlier selection algorithm on a Vector which will transform the high-dimensional input to a single Double output.
TransformDataSetOperation applies the stochastic outlier selection algorithm on a Vector which will transform the high-dimensional input to a single Double output.
Type of the input and output data which has to be a subtype of Vector
TransformDataSetOperation a single double which represents the oulierness of the input vectors, where the output is in [0, 1]