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Similaire à Graph based Semi Supervised Learning V1
Similaire à Graph based Semi Supervised Learning V1 (20)
Graph based Semi Supervised Learning V1
- 1. 2015 Graph Based Semi- Supervised Learning Neeta Pande, XRCI 3-Dec-2015 #GHCI15 2015
- 2. 2015 Outline Motivation for Graph-based Semi-Supervised Learning Core Concepts of Graph-based techniques Attribute Value Hyper-Graph Modelling The end to end pipeline for the techniques
- 3. 2015 Motivation Effective in propagating a limited amount of initial labels to a huge pool of unlabeled data (expensive to annotate) Many real life data sources are available as Graph Graph provides natural representation for multi-model, multi-format data from disparate sources Increasingly rich detailed and massive data sources need a promising paradigm for dealing with high-dimensional space
- 4. 2015 Core Concepts Vertices: Labeled and Unlabeled instances as nodes Connections: Pairwise Edges between vertices weighted by affinities (similarities) Prediction: Small portion of vertices carrying seed labels are harnessed to predict unlabeled vertices Information propagates from labeled data points, termed energy or heat Graph-based semi-supervised: Labelled and Unlabeled instances together with the graph structure is used in inference method
- 5. 2015 GSSL Algorithms Several popular GSSL algorithms exist − Graph Cuts, Graph-based random walks, manifold regularization and graph transduction Important Challenges − large scale data prevent adoption in practice − Noisy Contaminated labels Novel Technique: Spectral Graph Analytics with Hyper-Graph Heat diffusion Credit: Dr. Avinash Sharma
- 6. 2015 Fundamental Components of GSSL Graph Construction Information Propagation Framework Label Propagation and Inference Hypergraph Modelling Hyper-Graph Heat Kernel Framework & Sparsification Label Propagation and Inference Generalized GSSL techniques Hypergraph based heat diffusion technique
- 7. 2015 Customer he1 he2 he3 he4 v1 1 0 0 0 v2 1 0 1 0 v3 0 0 1 1 v4 0 0 1 0 v5 0 1 0 0 v6 0 1 1 1 v7 0 1 0 0 v8 0 1 0 1 Age (20-30) Age (30-40) Service (Voice) Service (Data) v4 v3 v1 v2 v5 v7 v6 he3 he2 he1 he4 v8 Hyper-graph Hyper-graph Incidence Matrix Hyper-Graph Modelling v7 v1 v6 v5 v2 v3 v4 v8 Simple-graph e1 e2 e3 e4 e6 e5
- 8. 2015 Connectivity in Graphs and Spectral Embedding Connectivity structure can be explored by random walks Projecting the graph into an isometric latent space (distance preserving) This space is spanned by Eigen vectors of graph Laplacian matrix
- 9. 2015 Heat- Diffusion HyperEdge Induction Label Propagation Label Inference The heat-diffusion pipeline Spectral Embedding Label, 𝑦 = 1 : 𝑐ℎ𝑢𝑟𝑛𝑒𝑟, -1 : 𝑙𝑜𝑦𝑎𝑙, 0 : 𝑢𝑛𝑘𝑛𝑜𝑤𝑛 Apply threshold on diffused labels • Scale dependent heat diffusion: known/unknown labels and outliers • SVD reduces high dimensional space, helping scale • Hypergraph modelling for scale and better similarity measure based on multiway relationships • Sparsification for noise reduction, scale and better similarity measure
- 10. 2015 Got Feedback? Rate and review the session on our mobile app – Convene For all details visit: http://ghcindia.anitaborg.org
Notes de l'éditeur
- We compute spectrum (eigen vector and eigen values) of hypergraph Laplacian matrix Use Laplacian to derive heat kernel matrix Diffuse the churn labels using the heat kernel matrix Infer churn labels by thresholding the diffused labels Optimizations: Sparsification Scale dependent heat diffusion for label propagation over hypergraphs: diffuse at a large scale when the known labels are small and vice-versa. Small scale diffusion would enforce label propagation in smaller neighborhoods & large vice-versa.
- Multiway relationships or higher order relations between the nodes Hyperedges can be used to link multiple instances/customers with same attribute value e.g. customers who bought same product/services
- Connectivity structure of a graph can be explored by random walks. A random walk on a graph is stochastic process which randomly jumps from vertex to vertex. Shortest path is not important. Average Reach through a s. connectivity should be average connectivity. Instead of simulating the exact random walk, which is a iterative process. At each node you have a probability that you can leave this node or which Arora, Akhil 1) stochasic process 13:53 2) thus one has to perform multiple iterations for the RWR distance to converge 3) After convergence, the values represent the avg. connectivity betweeen a pair of nodes
- Scale of label propagation governed by heat diffusion parameter t ** What is small churn and large chunr? Traditional graph and hypergraph based diffusion techniques diffuse at a fixed scale obtained by taking inverse of L and use it as diffusion matrix. Instead, we propose to use multi-scale heat diffusion based propagation parameterized by scale parameter t which governs scale of diffusion. Ans. Small number of training data i.e., number of customers with known churn labels.
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