The algebraic eigenvalue problem
The algebraic eigenvalue problem
An Analysis of Spectral Envelope Reduction via Quadratic Assignment Problems
SIAM Journal on Matrix Analysis and Applications
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
A Spectral Algorithm for Seriation and the Consecutive Ones Problem
SIAM Journal on Computing
A spectral method to separate disconnected and nearly-disconnected web graph components
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
Linearized cluster assignment via spectral ordering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Random matrices in data analysis
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
Stability of feature selection algorithms: a study on high-dimensional spaces
Knowledge and Information Systems
A tutorial on spectral clustering
Statistics and Computing
Top 10 algorithms in data mining
Knowledge and Information Systems
Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA
ECML '07 Proceedings of the 18th European conference on Machine Learning
Clustering based on matrix approximation: a unifying view
Knowledge and Information Systems
Non-negative matrix factorization for semi-supervised data clustering
Knowledge and Information Systems
Accelerating spectral clustering with partial supervision
Data Mining and Knowledge Discovery
Mind the eigen-gap, or how to accelerate semi-supervised spectral learning algorithms
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
ACM Transactions on Knowledge Discovery from Data (TKDD)
Feature selection for k-means clustering stability: theoretical analysis and an algorithm
Data Mining and Knowledge Discovery
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Several studies have demonstrated the prospects of spectral ordering for data mining. One successful application is seriation of paleontological findings, i.e. ordering the sites of excavation, using data on mammal co-occurrences only. However, spectral ordering ignores the background knowledge that is naturally present in the domain: paleontologists can derive the ages of the sites within some accuracy. On the other hand, the age information is uncertain, so the best approach would be to combine the background knowledge with the information on mammal co-occurrences. Motivated by this kind of partial supervision we propose a novel semi-supervised spectral ordering algorithm that modifies the Laplacian matrix such that domain knowledge is taken into account. Also, it performs feature selection by discarding features that contribute most to the unwanted variability of the data in bootstrap sampling. Moreover, we demonstrate the effectiveness of the proposed framework on the seriation of Usenet newsgroup messages, where the task is to find out the underlying flow of discussion. The theoretical properties of our algorithm are thoroughly analyzed and it is demonstrated that the proposed framework enhances the stability of the spectral ordering output and induces computational gains.