Parallelizing the QR algorithm for the unsymmetric algebraic eigenvalue problem: myths and reality
SIAM Journal on Scientific Computing
Chord: A scalable peer-to-peer lookup service for internet applications
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
A scalable content-addressable network
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
Computer Networking: A Top-Down Approach Featuring the Internet : Preliminary Edtion
Computer Networking: A Top-Down Approach Featuring the Internet : Preliminary Edtion
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Collective Principal Component Analysis from Distributed, Heterogeneous Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Incremental Isometric Embedding of High-Dimensional Data Using Connected Neighborhood Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributed similarity search in high dimensions using locality sensitive hashing
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
K-Landmarks: distributed dimensionality reduction for clustering quality maintenance
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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Data mining tasks results are usually improved by reducing the dimensionality of data This improvement however is achieved harder in the case that data lay on a non linear manifold and are distributed across network nodes Although numerous algorithms for distributed dimensionality reduction have been proposed, all assume that data reside in a linear space In order to address the non-linear case, we introduce D-Isomap, a novel distributed non linear dimensionality reduction algorithm, particularly applicable in large scale, structured peer-to-peer networks Apart from unfolding a non linear manifold, our algorithm is capable of approximate reconstruction of the global dataset at peer level a very attractive feature for distributed data mining problems We extensively evaluate its performance through experiments on both artificial and real world datasets The obtained results show the suitability and viability of our approach for knowledge discovery in distributed environments.