K
Krishnamoorthy Sivakumar
Researcher at Washington State University
Publications - 89
Citations - 2460
Krishnamoorthy Sivakumar is an academic researcher from Washington State University. The author has contributed to research in topics: Intersymbol interference & Additive white Gaussian noise. The author has an hindex of 20, co-authored 84 publications receiving 2329 citations. Previous affiliations of Krishnamoorthy Sivakumar include Johns Hopkins University & Texas A&M University.
Papers
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Proceedings ArticleDOI
On the privacy preserving properties of random data perturbation techniques
TL;DR: It is shown that random objects (particularly random matrices) have "predictable" structures in the spectral domain and it develops a random matrix-based spectral filtering technique to retrieve original data from the dataset distorted by adding random values.
Journal ArticleDOI
Random-data perturbation techniques and privacy-preserving data mining
TL;DR: This paper first notes that random matrices have predictable structures in the spectral domain and then it develops a random matrix-based spectral-filtering technique to retrieve original data from the dataset distorted by adding random values.
Journal ArticleDOI
Distributed clustering using collective principal component analysis
TL;DR: A way to integrate the Collective PCA with a given off-the-shelf clustering algorithm in order to develop a distributed clustering technique.
Journal ArticleDOI
Morphological operators for image sequences
TL;DR: A unifying approach to the problem of morphologically processing image sequences (or, equivalently, vector-valued images) by means of lattice theory, thus providing a mathematical foundation for vector morphology.
Book
Data Mining: Next Generation Challenges and Future Directions
TL;DR: This collection surveys the most recent advances in the field and charts directions for future research, discussing topics that include distributed data mining algorithms for new application areas, several aspects of next-generation data mining systems and applications, and detection of recurrent patterns in digital media.