kiez.hubness_reduction.DisSimLocal

class kiez.hubness_reduction.DisSimLocal(squared: bool = True, **kwargs)

Hubness reduction with DisSimLocal.

Uses the formula presented in [1].

Parameters:

squared (bool, default = True) – DisSimLocal operates on squared Euclidean distances. If True, return (quasi) squared Euclidean distances; if False, return (quasi) Eucldean distances.

References

[1]

Hara K, Suzuki I, Kobayashi K, Fukumizu K, Radovanović M (2016) Flattening the density gradient for eliminating spatial centrality to reduce hubness. In: Proceedings of the 30th AAAI conference on artificial intelligence, pp 1659-1665. https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/12055

__init__(squared: bool = True, **kwargs)

Methods

__init__([squared])
fit(source[, target])
kneighbors([k])
transform(neigh_dist, neigh_ind, query)	Transform distance between test and training data with DisSimLocal.

transform(neigh_dist, neigh_ind, query) → Tuple[T, T]

Transform distance between test and training data with DisSimLocal.

Parameters:

• neigh_dist (shape (n_query, n_neighbors)) – Distance matrix of test objects (rows) against their individual k nearest neighbors among the training data (columns).

• neigh_ind (shape (n_query, n_neighbors)) – Neighbor indices corresponding to the values in neigh_dist

• query (shape (n_query, n_features)) – Query entities that were used to obtain neighbors If none is provided use source that was provided in fit step

Returns:

DisSimLocal distances, and corresponding neighbor indices

Return type:

hub_reduced_dist, neigh_ind

Notes

The returned distances are NOT sorted! If you use this class directly, you will need to sort the returned matrices according to hub_reduced_dist.