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- 1. Generalization of Tensor Factorization and Applications Kohei Hayashi Collaborators:T. Takenouchi, T. Shibata, Y. Kamiya, D. Kato, K. Kunieda, K. Yamada, K. Ikeda, R. Tomioka, H. Kashima May 14, 2012 1 / 29
- 2. Relational datais a collection of relationships among multiple objects. relationships of pair ⇔ matrix 2 / 29
- 3. Relational datais a collection of relationships among multiple objects. relationships of pair ⇔ matrix relationships of 3-tuple ⇔ 3 dim. array 3 / 29
- 4. Relational datais a collection of relationships among multiple objects. relationships of pair ⇔ matrix relationships of 3-tuple ⇔ 3 dim. array tensor . . . . . .can represent as a tensor with missing values. 4 / 29
- 5. Issue of tensor representationTensor representation is generally high-dimensional andlarge-scale • e.g. 1, 000 users × 1, 000 items × 1, 000 times = total 1, 000, 000, 000 relationshipsDimensional reduction techniques such as tensorfactorization is used. 5 / 29
- 6. Tensor factorizationTucker decomposition [Tucker 1966]: a tensor factorizationmethod assuming 1 observation noise is Gaussian . 2 underlying tensor is low-dimensional .These assumptions are general ... but are not alwaystrue. 6 / 29
- 7. ContributionsGeneralize Tucker decomposition and propose two newmodels: 1 Exponential family tensor factorization (ETF) . [Joint work with Takenouchi, Shibata, Kamiya, Kunieda, Yamada, and Ikeda] • Generalize the noise distribution. • Can handle a tensor containing mixed discrete and continuous values. 2. Full-rank tensor completion (FTC) [Joint work with Tomioka and Kashima] • Kernelize Tucker decomposition • Complete missing values without reducing the dimensionality. 7 / 29
- 8. Outline1. Tucker decomposition2. Exponential family tensor factorization (ETF)3. Full-rank tensor completion (FTC)4. Conclusion 8 / 29
- 9. Outline1. Tucker decomposition2. Exponential family tensor factorization (ETF)3. Full-rank tensor completion (FTC)4. Conclusion 9 / 29
- 10. Tucker decomposition K1 K2 K3 (1) (2) (3) xijk = uiq ujr uks zqrs + εijk (1) q=1 r=1 s=1• ε: i.i.d Gaussian noise 10 / 29
- 11. Tucker decomposition K1 K2 K3 (1) (2) (3) xijk = uiq ujr uks zqrs + εijk (1) q=1 r=1 s=1• ε: i.i.d Gaussian noise 11 / 29
- 12. Tucker decomposition K1 K2 K3 (1) (2) (3) xijk = uiq ujr uks zqrs + εijk (1) q=1 r=1 s=1• ε: i.i.d Gaussian noise 12 / 29
- 13. Vectorized formLet x ∈ RD and z ∈ RK denote vectorized X and Z,resp., then x = Wz + ε where W ≡ U(3) ⊗ U(2) ⊗ U(1) . • D ≡ D1 D2 D3 , K ≡ K1 K2 K3 • ⊗: the Kronecker productTucker decomposition = A linear Gaussian model x ∼ N (x | Wz, σ 2 I) 13 / 29
- 14. Rank of tensorCall dimensionalities of the core tensor rank of tensor . • Rank of X is (K1 , K2 , K3 ).Rank of tensor represents its complexity • low-rank tensor has less information 14 / 29
- 15. Outline1. Tucker decomposition2. Exponential family tensor factorization (ETF)3. Full-rank tensor completion (FTC)4. Conclusion 15 / 29
- 16. Motivation: heterogeneous tensorTucker decomposition assumes Gaussian noise • not appropriate for such data 16 / 29
- 17. Motivation: heterogeneous tensorTucker decomposition assumes Gaussian noise • not appropriate for such data.Approach.generalize Tucker decomposition, assuming a diﬀerentdistribution for each element. 17 / 29
- 18. Exponential family tensor factorization.Likelihood. D x∼ Expond (xd | θd ), θ ≡ Wz d=1 exp. family Tucker decomp.. where Expon(x | θ) ≡ exp [xθ − ψ(θ) + F (x)]exponential family : a class of distributions Gaussian Poisson Bernoulli 0.4 0.7 Density Density 0.2 0.2 0.5 (θ = 0) 0.0 0.0 0.3 ψ(θ) θ2−4 −2 /2 0 2 4 exp[θ] 4 0 2 6 8 ln(10.0 0.5 1.0 1.5 −0.5 + exp[θ]) x 18 / 29
- 19. .Priors. • for z: a Gaussian prior N (0, I) (m) −1. • for U : a Gaussian prior N (0, αm I).Joint log-likelihood. D L =x Wz − ψhd (wd z) d=1 M 1 αm 2 − ||z||2 − U(m) + const. 2 m=1 2 Fro.Estimate parameters by Bayesian inference. 19 / 29
- 20. Bayesian inferenceMarginal-MAP estimator: argmax exp[L(z, U(1) , . . . , U(M ) )]dz U(1) ,...,U(M ) • The integral is not analytical.Develop eﬃcient yet accurate approximation withLaplace approximation and Gaussian process. • Computational cost is still higher than Tucker dcomp. • For a long and thin tensor (e.g. time series), online algorithm is applicable (see thesis.) 20 / 29
- 21. Experiments 21 / 29
- 22. Anomaly detection by ETF Purpose Find irregular parts of tensor data Method Apply distance-based outlier DB (p, D) [Knorr+ VLDB’00] to the estimated factor matrix U(m).Deﬁnition of DB (p, D).“An object O is a DB (p, D) outlier if at least fraction p. the objects lies at a distance greater than D from O.”of 22 / 29
- 23. Data setA time series of multisensor measurements • Each sensor recorded the human behavior (e.g. position) of researchers in NEC lab. for 8 month. • 6 (sensors) × 21 (persons) × 1927 (hours) Sensor Type Min Max X1 # of sent emails Count 0.00 14.00 X2 # of received emails Count 0.00 15.00 X3 # of typed keys Count 0.00 50422.00 X4 X coordinate Real −550.17 3444.15 X5 Y coordinate Real 128.71 2353.55 X6 Movement distance Non-negative 0.00 203136.96 23 / 29
- 24. EvaluationList of irregular events are provided. Examples of irregular events Date Time Description Dec 21 All day Private incident Dec 22 15:00 Monthly seminar 16:00 Jun 15 13:00 Visiting tour . . . . . . . . . • Evaluate as a binary classiﬁcation • 50% are missing, 10 trials • Apply ETF with online algorithm 24 / 29
- 25. Result: classiﬁcation performance 0.7 method 0.6 PARAFAC AUC Tucker pTucker (EM) 0.5 ETF online 0.4 2x2x2 3x3x3 4x4x4 5x5x5 6x6x6 6x7x7 Rank 25 / 29
- 26. Result: classiﬁcation performance 0.7 method 0.6 PARAFAC AUC Tucker pTucker (EM) 0.5 ETF online 0.4 2x2x2 3x3x3 4x4x4 5x5x5 6x6x6 6x7x7 Rank ETF well detect anomaly events 26 / 29
- 27. Skip the rest 27 / 29

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