Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
This map shows the geographic impact of Partha Niyogi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Partha Niyogi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Partha Niyogi more than expected).
This network shows the impact of papers produced by Partha Niyogi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Partha Niyogi. The network helps show where Partha Niyogi may publish in the future.
Co-authorship network of co-authors of Partha Niyogi
This figure shows the co-authorship network connecting the top 25 collaborators of Partha Niyogi.
A scholar is included among the top collaborators of Partha Niyogi based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Partha Niyogi. Partha Niyogi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Niyogi, Partha. (2013). Manifold regularization and semi-supervised learning: some theoretical analyses. Journal of Machine Learning Research. 14(1). 1229–1250.58 indexed citations
2.
Sonderegger, Morgan & Partha Niyogi. (2010). Combining Data and Mathematical Models of Language Change. Meeting of the Association for Computational Linguistics. 1019–1029.4 indexed citations
3.
Rosenberg, David S., Vikas Sindhwani, Peter L. Bartlett, & Partha Niyogi. (2009). Multiview point cloud kernels for semisupervised learning. IEEE Signal Processing Magazine.3 indexed citations
4.
Narayanan, Hariharan & Partha Niyogi. (2009). On the sample complexity of learning smooth cuts on a manifold. Conference on Learning Theory.4 indexed citations
5.
Agarwal, Shivani & Partha Niyogi. (2009). Generalization Bounds for Ranking Algorithms via Algorithmic Stability. Journal of Machine Learning Research. 10(16). 441–474.95 indexed citations
Belkin, Mikhail & Partha Niyogi. (2003). Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation. 15(6). 1373–1396.4803 indexed citations breakdown →
15.
Belkin, Mikhail & Partha Niyogi. (2002). Using manifold structure for partially labelled classification. Neural Information Processing Systems. 953–960.63 indexed citations
16.
Belkin, Mikhail & Partha Niyogi. (2002). Using Manifold Stucture for Partially Labeled Classification. Neural Information Processing Systems. 15. 953–960.43 indexed citations
17.
Niyogi, Partha, E. Petajan, & Jialin Zhong. (1999). Feature based representation for audio-visual speech recognition.. AVSP. 16.12 indexed citations
18.
Niyogi, Partha & Robert C. Berwick. (1996). Learning from triggers. Linguistic Inquiry. 27(4). 605–622.27 indexed citations
19.
Sung, Kah Kay & Partha Niyogi. (1995). A Formulation for Active Learning with Applications to Object Detection. DSpace@MIT (Massachusetts Institute of Technology).7 indexed citations
20.
Sung, Kah Kay & Partha Niyogi. (1994). Active Learning for Function Approximation. Neural Information Processing Systems. 593–600.19 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.