6 citations · 11 across the 13 of their papers we have counts for
18 papers
Improved Outlier Robust Seeding for k-means
Amit Deshpande, Rameshwar Pratap
The -means is a popular clustering objective, although it is inherently non-robust and sensitive to outliers. Its popular seeding or initialization called -means++ uses $D^{2…
Minwise-Independent Permutations with Insertion and Deletion of Features
Rameshwar Pratap, Raghav Kulkarni
In their seminal work, Broder \textit{et. al.}~\citep{BroderCFM98} introduces the algorithm that computes a low-dimensional sketch of high-dimensional binary dat…
One-pass additive-error subset selection for subspace approximation
Amit Deshpande, Rameshwar Pratap
We consider the problem of subset selection for subspace approximation, that is, to efficiently find a \emph{small} subset of data points such that solving the problem o…
Improving \textit{Tug-of-War} sketch using Control-Variates method
Rameshwar Pratap, Bhisham Dev Verma, Raghav Kulkarni
Computing space-efficient summary, or \textit{a.k.a. sketches}, of large data, is a central problem in the streaming algorithm. Such sketches are used to answer \textit{post-hoc} q…
A revised comparison between FF five-factor model and three-factor model,based on China's A-share market
Zhijing Zhang, Yue Yu, Qinghua Ma +1
In allusion to some contradicting results in existing research, this paper selects China's latest stock data from 2005 to 2020 for empirical analysis. By choosing this periods' dat…
Efficient Binary Embedding of Categorical Data using BinSketch
Bhisham Dev Verma, Rameshwar Pratap, Debajyoti Bera
In this work, we present a dimensionality reduction algorithm, aka. sketching, for categorical datasets. Our proposed sketching algorithm Cabin constructs low-dimensional binary sk…