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SDM
2009
SIAM

180views Data Mining» more SDM 2009»

Multi-Modal Hierarchical Dirichlet Process Model for Predicting Image Annotation and Image-Object Label Correspondence.

1 years 11 months ago

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Many real-world applications call for learning predictive relationships from multi-modal data. In particular, in multi-media and web applications, given a dataset of images and th...

Oksana Yakhnenko, Vasant Honavar

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Id	Views	Title	Status
1	180	Multi-Modal Hierarchical Dirichlet Process Model for Predicting Image Annotation and Image-Object Label Correspondence.	claim
2	169	Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation.	claim
3	120	A Hybrid Data Mining Metaheuristic for the p-Median Problem.	claim
4	94	Privacy Preservation in Social Networks with Sensitive Edge Weights.	claim
5	90	High Performance Parallel/Distributed Biclustering Using Barycenter Heuristic.	claim
6	80	Application of Bayesian Partition Models in Warranty Data Analysis.	claim
7	76	Topic Cube: Topic Modeling for OLAP on Multidimensional Text Databases.	claim
8	75	A Framework for Exploring Categorical Data.	claim
9	74	Low-Entropy Set Selection.	claim
10	67	Discretized Spatio-Temporal Scan Window.	claim
11	67	Bayesian Cluster Ensembles.	claim
12	66	Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations.	claim
13	65	Hybrid Clustering of Text Mining and Bibliometrics Applied to Journal Sets.	claim
14	63	Context Aware Trace Clustering: Towards Improving Process Mining Results.	claim
15	59	Discovering Substantial Distinctions among Incremental Bi-Clusters.	claim
16	58	A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks.	claim
17	57	Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction.	claim
18	56	MultiVis: Content-Based Social Network Exploration through Multi-way Visual Analysis.	claim
19	56	Detection and Characterization of Anomalies in Multivariate Time Series.	claim
20	56	Understanding Importance of Collaborations in Co-authorship Networks: A Supportiveness Analysis Approach.	claim
21	56	Discovery of Geospatial Discriminating Patterns from Remote Sensing Datasets.	claim
22	55	Learning Random-Walk Kernels for Protein Remote Homology Identification and Motif Discovery.	claim
23	54	Near-optimal Supervised Feature Selection among Frequent Subgraphs.	claim
24	53	Detecting Communities in Social Networks Using Max-Min Modularity.	claim
25	53	Finding Links and Initiators: A Graph-Reconstruction Problem.	claim
26	51	DensEst: Density Estimation for Data Mining in High Dimensional Spaces.	claim
27	51	Mining Complex Spatio-Temporal Sequence Patterns.	claim
28	50	Topic Evolution in a Stream of Documents.	claim
29	50	Diversity-Based Weighting Schemes for Clustering Ensembles.	claim
30	50	Top-k Correlative Graph Mining.	claim
31	49	Polynomial-Delay and Polynomial-Space Algorithms for Mining Closed Sequences, Graphs, and Pictures in Accessible Set Systems.	claim
32	46	Feature Weighted SVMs Using Receiver Operating Characteristics.	claim
33	46	Optimal Distance Bounds on Time-Series Data.	claim
34	46	Proximity-Based Anomaly Detection Using Sparse Structure Learning.	claim
35	44	Speeding Up Secure Computations via Embedded Caching.	claim
36	44	Finding Representative Association Rules from Large Rule Collections.	claim
37	44	A New Constraint for Mining Sets in Sequences.	claim
38	44	Mining Cohesive Patterns from Graphs with Feature Vectors.	claim
39	44	Agglomerative Mean-Shift Clustering via Query Set Compression.	claim
40	43	CORE: Nonparametric Clustering of Large Numeric Databases.	claim
41	43	The Metric Dilemma: Competence-Conscious Associative Classification.	claim
42	43	Exact Discovery of Time Series Motifs.	claim
43	42	Structure and Dynamics of Research Collaboration in Computer Science.	claim
44	42	Non-negative Matrix Factorization, Convexity and Isometry.	claim
45	42	Multiple Kernel Clustering.	claim
46	42	Tracking User Mobility to Detect Suspicious Behavior.	claim
47	41	Event Discovery in Time Series.	claim
48	41	Constraint-Based Subspace Clustering.	claim
49	41	Travel-Time Prediction Using Gaussian Process Regression: A Trajectory-Based Approach.	claim
50	41	Graph Generation with Prescribed Feature Constraints.	claim
51	41	Adaptive Concept Drift Detection.	claim
52	41	Scalable Distributed Change Detection from Astronomy Data Streams Using Local, Asynchronous Eigen Monitoring Algorithms.	claim
53	41	Parallel Pairwise Clustering.	claim
54	39	On Segment-Based Stream Modeling and Its Applications.	claim
55	39	Parallel Large Scale Feature Selection for Logistic Regression.	claim
56	39	Identifying Information-Rich Subspace Trends in High-Dimensional Data.	claim
57	38	Randomization Techniques for Graphs.	claim
58	38	Providing Privacy through Plausibly Deniable Search.	claim
59	38	Highlighting Diverse Concepts in Documents.	claim
60	38	Time-Decayed Correlated Aggregates over Data Streams.	claim
61	37	On Randomness Measures for Social Networks.	claim
62	37	Non-parametric Information-Theoretic Measures of One-Dimensional Distribution Functions from Continuous Time Series.	claim
63	37	Hierarchical Linear Discriminant Analysis for Beamforming.	claim
64	36	AMORI: A Metric-Based One Rule Inducer.	claim
65	36	MUSK: Uniform Sampling of k Maximal Patterns.	claim
66	36	Spatially Cost-Sensitive Active Learning.	claim
67	35	Local Relevance Weighted Maximum Margin Criterion for Text Classification.	claim
68	35	FuncICA for Time Series Pattern Discovery.	claim
69	35	On the Comparison of Relative Clustering Validity Criteria.	claim
70	35	A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning.	claim
71	35	An Entity Based Model for Coreference Resolution.	claim
72	35	A Semi-Supervised Framework for Feature Mapping and Multiclass Classification.	claim
73	35	Grammar Mining.	claim
74	34	Multi-topic Based Query-Oriented Summarization.	claim
75	33	GAD: General Activity Detection for Fast Clustering on Large Data.	claim
76	33	Measuring Discrimination in Socially-Sensitive Decision Records.	claim
77	33	ShatterPlots: Fast Tools for Mining Large Graphs.	claim
78	33	Exploiting Semantic Constraints for Estimating Supersenses with CRFs.	claim
79	33	Identifying Unsafe Routes for Network-Based Trajectory Privacy.	claim
80	32	Straightforward Feature Selection for Scalable Latent Semantic Indexing.	claim
81	30	Twin Vector Machines for Online Learning on a Budget.	claim

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