Hubness in the Context of Feature Selection and Generation

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Hubness in the Context of Feature Selection and Generation

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Hubness in the Context of Feature Selection and Generation

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Hubness in the Context of Feature Selection and Generation

Miloš Radovanović1 Alexandros Nanopoulos2

Mirjana Ivanović1

1Department of Mathematics and Informatics

Faculty of Science, University of Novi Sad, Serbia

2Institute of Computer Science

University of Hildesheim, Germany

Nk(x), the number of k-occurrences of point x, is the number of times x occurs among k nearest neighbors of all other points in a data set

- Nk(x) is the in-degree of node x in the k-NN digraph
It was observed that the distribution of Nk can become skewed, resulting in the emergence of hubs – points with high Nk

- Music retrieval [Aucouturier 2007]
- Speech recognition [Doddington 1998]
- Fingerprint identification [Hicklin 2005]

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What causes the skewness of Nk?

- Artefact of data?
- Are some songs more similar to others?
- Do some people have fingerprints or voices that are harder to distinguish from other people’s?

- Specifics of modeling algorithms?
- Inadequate choice of features?

- Something more general?

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Demonstrate the phenomenon

- Skewness in the distr of k-occurrences
Explain its main reasons

- No artifact of data
- No specifics of models (inadequate features, etc.)
- A new aspect of the „curse of dimensionality“
Impact on Feature Selection and Generation

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Demonstrate the phenomenon

Explain its main reasons

Impact on FSG

Conclusions

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SNk is standardized 3rd moment of Nk

If SNk = 0 no skew, positive (negative) values signify right (left) skew

High skewness indicates hubness

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Demonstrate the phenomenon

Explain its main reasons

Impact on IR

Conclusions

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Spearman correlation between N10 and distance from data set mean

Hubs are closer to the data center

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- Hubs due to centrality
- vectors closer to the center tend to be closer to all other vectors
- thus more frequent k-NN

- Centrality is amplified by dimensionality

point A closer to center than point B

∑ sim(A,x) - ∑ sim(B,x)

x

x

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Concentration: as dim grows to infinity

- Ratio between standard deviation of pairwise similarities (distances) and their expectation shrinks to zero
- Minkowski [François 2007, Beyer 1999, Aggarwal 2001]
- Meaningfulness of nearest neighbors?
Analytical proof for cosine sim [Radovanović 2010]

- Meaningfulness of nearest neighbors?

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E

√V

Hyper-sphere view

- Most vectors are about equidistant from the center and from each other, and lie on the surface of a hyper-sphere
- Few vectors lie at the inner part of hyper-sphere, closer to its center, thus closer to all others
- This is expected for large but finite dimensionality, since is non negligible

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Spearman correlation between N10 anddistance from data/cluster center

Real text data are usually clustered (mixture of distributions)

Cluster with k-Means (#clusters = 3*Cls)

Compare with

Generalization of the hyper-sphere view with clusters

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Intrinsic dimensionalityis reached

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Demonstrate the phenomenon

Explain its main reasons

Impact on FSG

Conclusions

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- Based on information about classes,k-occurrences can be distinguished into:
- “Bad” k-occurrences, BNk(x)
- “Good” k-occurrences, GNk(x)
- Nk(x) = BNk(x) + GNk(x)

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- Mixture is important also:
- High dimensionality and skewness of Nkdo not automatically induce “badness”
- “Bad” hubs originate from a combination of high dimensionality and violation of the CA
- Cluster Assumption (CA): Most pairs of vectors in a cluster should be of the same class [Chapelle 2006]

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Skewness stays relatively constant

It abruptly drops when intrinsic dimensionality is reached

Further feature selection may incur loss of information.

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Similar observations

When reaching intrinsic dimensionality, BNk ratio increases

The representation ceases to reflect the information provided by labels very well

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When adding features to bring new information to the data:

- Representation will ultimately increase SNkand, thus, produce hubs
- The reduction of BNk ratio “flattens out” fairly quickly, limiting the usefulness of adding new features in the sense of being able to express the “ground truth”
If instead of BNk ratio we use classifier error rate, the results are similar

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- Little attention by research in feature selection/ generation to the fact that in intrinsically high-dimensional data, hubs will :
- Result in an uneven distribution of the cluster assumption violation (hubs will be generated that attract more label mismatches with neighborin points)
- Result in an uneven distribution of responsibility for classification or retrieval error among data points.

- Investigating further the interaction between:
- hubness and
- different notions of CA violation

- Important new insights into feature selection/generation

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