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CSI :Florida A BAYESIAN APPROACH TO LOCALIZED MULTI-KERNEL LEARNING USING THE RELEVANCE VECTOR MACHINE R. Close, J. Wilson, P. Gader.

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Presentation on theme: "CSI :Florida A BAYESIAN APPROACH TO LOCALIZED MULTI-KERNEL LEARNING USING THE RELEVANCE VECTOR MACHINE R. Close, J. Wilson, P. Gader."— Presentation transcript:

1 CSI :Florida A BAYESIAN APPROACH TO LOCALIZED MULTI-KERNEL LEARNING USING THE RELEVANCE VECTOR MACHINE R. Close, J. Wilson, P. Gader

2 Outline Benefits of kernel methods Multi-kernels and localized multi-kernels Relevance Vector Machines (RVM) Localized multi-kernel RVM (LMK-RVM) Application of LMK-RVM to landmine detection Conclusions 2

3 CSI :Florida Kernel Methods Overview Using a non-linear mapping a decision surface can become linear in a transformed space 3

4 CSI :Florida Kernel Methods Overview If the mapping satisfies Mercers theorem (i.e., the it is finitely positive-definite) then it corresponds to an inner-product kernel K 4

5 CSI :Florida Kernel Methods Feature transformations increase dimensionality to create a linear separation between classes Utilizing the kernel trick, kernel methods construct these feature transformations in an infinite dimensional space that can be finitely characterized The accuracy and robustness of the model becomes directly dependent on the kernels ability to represent the correlation between data points –A side benefit is an increased understanding of the latent relationships between data points once the kernel parameters are learned 5

6 CSI :Florida Multi-Kernel Learning When using kernel methods, a specific form of kernel function is chosen (e.g. a radial basis function). Multi-kernel learning uses a linear combination of kernel functions – –The weights may be constrained if desired As the model is trained, the weights yielding the best input-space to kernel-space mapping are learned. Any kernel function whose weight approaches 0 is pruned out of the multi-kernel function. 6

7 CSI :Florida Localized Multi-Kernel Learning Localized multi-kernel (LMK) learning allows different kernels (or different kernel parameters) to be used in separate areas of the feature space. Thus the model is not limited to the assumption that one kernel function can effectively map the entire feature-space Many LMK approaches attempt to simultaneously partition the feature-space and learn the multi-kernel Different Multi-kernels 7

8 CSI :Florida LMK-RVM A localized multi-kernel relevance vector machine (LMK- RVM) uses the ARD (automatic relevance determination) prior of the RVM to select the kernels to use over a given feature-space. This allows greater flexibility in the localization of the kernels and increased sparsity 8

9 CSI :Florida RVM Overview LikelihoodPosteriorARD Prior 9

10 CSI :Florida RVM Overview LikelihoodPosteriorARD Prior Note the vector hyper-parameter 10

11 CSI :Florida Automatic Relevance Determination Values for and are determined by integrating over the weights, and maximizing the resulting marginal distribution. Those training samples that do not help predict the output of other training samples have α values that tend toward infinity. Their associated w priors become δ functions with mean 0, that is, their weight in predicting outcomes at other points should be exactly 0. Thus, these training vectors can be removed. We can use the remaining, relevant, vectors to estimate the outputs associated with new data. The design matrix K= Φ is now NxM, where M<

12 CSI :Florida RVM for Classification Start with a two-class problem –t {0,1} – ( ) is logistic sigmoid Same as RVM for regression except must use IRLS to calculate the mode of the posterior distribution – 12

13 CSI :Florida LMK-RVM Using the multi-kernel with the RVM model, we start with: where w n is the weight on the multi-kernel associated with vector n and w i is the weight on the i th component of each multi-kernel. Unlike some kernel methods (e.g. SVM) the RVM is not constrained to use a positive-definite kernel matrix, thus, there is no requirement that the weights be factorized as w n w i. So, in this setting We show a sample application of LMK-RVM using two radial basis kernels at each training point with different spreads. 13

14 CSI :Florida Toy Dataset Example Kernels with larger σ Kernels with smaller σ 14

15 CSI :Florida GPR Data Experiments GPR experiments using data with120 dimension spectral features Improvements in classification happen off-diagonal 15

16 CSI :Florida GPR ROC 16

17 CSI :Florida WEMI Data Experiments WEMI experiments using data with 3 dimension GRANMA features Improvements in classification happen off-diagonal 17

18 CSI :Florida WEMI ROC 18

19 CSI :Florida Number of Relevant Vectors WEMIGPR Number of relevant vectors averaged over all ten folds. The off-diagonal shows a potentially sparser model 19

20 CSI :Florida Conclusions The experiment using GPR data features showed that LMK-RVM can provide definite improvement in SSE, AUC, and the ROC The experiment using the lower-dimensional WEMI data GRANMA features showed that using the same LMK- RVM method provided some improvement in SSE and AUC and an inconclusive ROC Both set of experiments show the potential for sparser models when choosing when using the LMK-RVM Question: is there an effective way to learn values for spreads in our simple class of localized multi-kernels? 20

21 CSI :Florida References [1]F. R. Bach, et al., "Multiple Kernel Learning, Conic Duality, and the SMO Algorithm," in International Conference on Machine Learning, Banff, Canada, [2]T. Damoulas, et al., "Inferring Sparse Kernel Combinations and Relevance Vectors: An Application to Subcellular Localization of Proteins," in Machine Learning and Applications, ICMLA '08. Seventh International Conference on, 2008, pp [3]G. Camps-Valls, et al., "Nonlinear System Identification With Composite Relevance Vector Machines," Signal Processing Letters, IEEE, vol. 14, pp , [4]B. Wu, et al., "A Genetic Multiple Kernel Relevance Vector Regression Approach," in Education Technology and Computer Science (ETCS), 2010 Second International Workshop on, 2010, pp [5]R. A. Jacobs, et al., "Adaptive Mixtures of Local Experts," Neural Computation, vol. 3, pp , [6]C. E. Rasmussen and Z. Ghahramani, "Infinite Mixtures of Gaussian Process Experts," in Advances in Neural Information Processing Systems,

22 CSI :Florida References [7]L. Yen-Yu, et al., "Local Ensemble Kernel Learning for Object Category Recognition," in Computer Vision and Pattern Recognition, CVPR '07. IEEE Conference on, 2007, pp [8]M. Gonen and E. Alpaydin, "Localized Multiple Kernel Learning," in 25th International Conference on Machine Learning, Helsinki, Finland, [9]M. Gonen and E. Alpaydin, "Localized Multiple Kernel Regression," in Pattern Recognition (ICPR), th International Conference on, 2010, pp [10]M. E. Tipping, "The Relevance Vector Machine," Advances in Neural Information Processing Systems, vol. 12, pp , [11]C. M. Bishop, "Relevance Vector Machines (Analysis of Sparsity)," in Pattern Recognition and Machine Learning, ed: Springer, 2007, pp [12]D. Tzikas, A. Likas, and N. Galatsanos. Large Scale Multikernel Relevance Vector Machine for Object Detection, International Journal on Artificial Intelligence Tools, 16(6): , December [13]D. Tzikas, A. Likas, and N. Galatsanos, "Large Scale Multikernel RVM for Object Detection," presented at the Hellenic Conference on Artificial Intelligence, Heraclion, Crete, Greece,

23 CSI :Florida Backup Slides Expanded Kernels Discussion 23

24 CSI :Florida In both these problems linear classification methods have difficulty discriminating the blue class from the others! What is the actual problem here? -No one line can separate the blue class from the other datapoints! Similar to the single-layer perceptron problem (The XOR problem)! Kernel Methods Example: The Masked Class Problem 24

25 CSI :Florida Decision Surface in Feature Space Can classify the green and black class with no problem! Problems when we try to classify the blue class!!!! 25

26 CSI :Florida Revisit Masked Class Problem Are linear methods completely useless on this data? -No, we can perform a non-linear transformation on the data via fixed basis functions! -Many times when we perform this transformation features that where not linearly separable in the original feature space become linearly separable in the transformed feature space. 26

27 CSI :Florida Basis Functions Models can be extended by using fixed basis functions which allows for linear combinations of nonlinear functions of the input variables – –Gaussian (or RBF) basis function: –Basis vector: –Dummy basis function used for bias parameter: –Basis function center ( ) governs location in input space –Scale parameter ( ) determines spatial scale 27

28 CSI :Florida Features in Transformed Space are Linearly Separable Transformed datapoints are plotted in the new feature space 28

29 CSI :Florida Transformed Decision Surface in Feature Space Again, we can classify the green and black class with no problem! Now we can classify the blue class with no problem!!! 29

30 CSI :Florida Common Kernels Squared Exponential: Gaussian Process Kernel: Automatic Relevance Determination (ARD) kernel Other kernels: –Neural Network –Matern – -exponential, etc. 30


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