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SDM
2009
SIAM

394views Data Mining» more SDM 2009»

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

14 years 9 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	394	Multi-Modal Hierarchical Dirichlet Process Model for Predicting Image Annotation and Image-Object Label Correspondence.	claim
2	343	Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation.	claim
3	331	Privacy Preservation in Social Networks with Sensitive Edge Weights.	claim
4	291	Detection and Characterization of Anomalies in Multivariate Time Series.	claim
5	251	High Performance Parallel/Distributed Biclustering Using Barycenter Heuristic.	claim
6	235	Topic Cube: Topic Modeling for OLAP on Multidimensional Text Databases.	claim
7	225	Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations.	claim
8	223	Context Aware Trace Clustering: Towards Improving Process Mining Results.	claim
9	220	Bayesian Cluster Ensembles.	claim
10	219	A Hybrid Data Mining Metaheuristic for the p-Median Problem.	claim
11	217	A Framework for Exploring Categorical Data.	claim
12	215	Hybrid Clustering of Text Mining and Bibliometrics Applied to Journal Sets.	claim
13	208	Topic Evolution in a Stream of Documents.	claim
14	208	A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks.	claim
15	205	Identifying Information-Rich Subspace Trends in High-Dimensional Data.	claim
16	204	Application of Bayesian Partition Models in Warranty Data Analysis.	claim
17	202	Proximity-Based Anomaly Detection Using Sparse Structure Learning.	claim
18	196	MultiVis: Content-Based Social Network Exploration through Multi-way Visual Analysis.	claim
19	193	Agglomerative Mean-Shift Clustering via Query Set Compression.	claim
20	192	Mining Cohesive Patterns from Graphs with Feature Vectors.	claim
21	191	Adaptive Concept Drift Detection.	claim
22	185	Understanding Importance of Collaborations in Co-authorship Networks: A Supportiveness Analysis Approach.	claim
23	184	DensEst: Density Estimation for Data Mining in High Dimensional Spaces.	claim
24	180	Structure and Dynamics of Research Collaboration in Computer Science.	claim
25	180	Hierarchical Linear Discriminant Analysis for Beamforming.	claim
26	179	Parallel Large Scale Feature Selection for Logistic Regression.	claim
27	176	Constraint-Based Subspace Clustering.	claim
28	176	Discovery of Geospatial Discriminating Patterns from Remote Sensing Datasets.	claim
29	175	Low-Entropy Set Selection.	claim
30	173	Discretized Spatio-Temporal Scan Window.	claim
31	172	Travel-Time Prediction Using Gaussian Process Regression: A Trajectory-Based Approach.	claim
32	170	Optimal Distance Bounds on Time-Series Data.	claim
33	170	Mining Complex Spatio-Temporal Sequence Patterns.	claim
34	167	Detecting Communities in Social Networks Using Max-Min Modularity.	claim
35	167	Parallel Pairwise Clustering.	claim
36	164	Exact Discovery of Time Series Motifs.	claim
37	164	Time-Decayed Correlated Aggregates over Data Streams.	claim
38	162	Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction.	claim
39	162	Diversity-Based Weighting Schemes for Clustering Ensembles.	claim
40	161	Feature Weighted SVMs Using Receiver Operating Characteristics.	claim
41	161	Polynomial-Delay and Polynomial-Space Algorithms for Mining Closed Sequences, Graphs, and Pictures in Accessible Set Systems.	claim
42	160	Discovering Substantial Distinctions among Incremental Bi-Clusters.	claim
43	157	MUSK: Uniform Sampling of k Maximal Patterns.	claim
44	154	AMORI: A Metric-Based One Rule Inducer.	claim
45	152	Non-negative Matrix Factorization, Convexity and Isometry.	claim
46	152	Multiple Kernel Clustering.	claim
47	149	Speeding Up Secure Computations via Embedded Caching.	claim
48	149	Near-optimal Supervised Feature Selection among Frequent Subgraphs.	claim
49	144	On Segment-Based Stream Modeling and Its Applications.	claim
50	144	CORE: Nonparametric Clustering of Large Numeric Databases.	claim
51	143	Finding Representative Association Rules from Large Rule Collections.	claim
52	140	Straightforward Feature Selection for Scalable Latent Semantic Indexing.	claim
53	138	ShatterPlots: Fast Tools for Mining Large Graphs.	claim
54	138	The Metric Dilemma: Competence-Conscious Associative Classification.	claim
55	130	FuncICA for Time Series Pattern Discovery.	claim
56	130	Grammar Mining.	claim
57	129	Multi-topic Based Query-Oriented Summarization.	claim
58	129	Scalable Distributed Change Detection from Astronomy Data Streams Using Local, Asynchronous Eigen Monitoring Algorithms.	claim
59	127	Event Discovery in Time Series.	claim
60	127	Learning Random-Walk Kernels for Protein Remote Homology Identification and Motif Discovery.	claim
61	126	An Entity Based Model for Coreference Resolution.	claim
62	125	Providing Privacy through Plausibly Deniable Search.	claim
63	125	Identifying Unsafe Routes for Network-Based Trajectory Privacy.	claim
64	124	Non-parametric Information-Theoretic Measures of One-Dimensional Distribution Functions from Continuous Time Series.	claim
65	123	Measuring Discrimination in Socially-Sensitive Decision Records.	claim
66	123	Randomization Techniques for Graphs.	claim
67	123	Tracking User Mobility to Detect Suspicious Behavior.	claim
68	119	Twin Vector Machines for Online Learning on a Budget.	claim
69	118	Finding Links and Initiators: A Graph-Reconstruction Problem.	claim
70	117	Spatially Cost-Sensitive Active Learning.	claim
71	114	GAD: General Activity Detection for Fast Clustering on Large Data.	claim
72	114	On the Comparison of Relative Clustering Validity Criteria.	claim
73	114	Top-k Correlative Graph Mining.	claim
74	113	Local Relevance Weighted Maximum Margin Criterion for Text Classification.	claim
75	113	Graph Generation with Prescribed Feature Constraints.	claim
76	112	A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning.	claim
77	111	A New Constraint for Mining Sets in Sequences.	claim
78	108	Highlighting Diverse Concepts in Documents.	claim
79	107	A Semi-Supervised Framework for Feature Mapping and Multiclass Classification.	claim
80	105	Exploiting Semantic Constraints for Estimating Supersenses with CRFs.	claim
81	104	On Randomness Measures for Social Networks.	claim

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