4 citations · 9 across the 6 of their papers we have counts for
7 papers
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active Learning
Hankui Peng, Nicos G. Pavlidis
Spectral-based subspace clustering methods have proved successful in many challenging applications such as gene sequencing, image recognition, and motion segmentation. In this work…
Adaptive Dynamic Model Averaging with an Application to House Price Forecasting
Alisa Yusupova, Nicos G. Pavlidis, Efthymios G. Pavlidis
Dynamic model averaging (DMA) combines the forecasts of a large number of dynamic linear models (DLMs) to predict the future value of a time series. The performance of DMA critical…
Subspace Clustering with Active Learning
Hankui Peng, Nicos G. Pavlidis
Subspace clustering is a growing field of unsupervised learning that has gained much popularity in the computer vision community. Applications can be found in areas such as motion…
Robust Function-on-Function Regression
Harjit Hullait, David S. Leslie, Nicos G. Pavlidis +1
Functional linear regression is a widely used approach to model functional responses with respect to functional inputs. However, classical functional linear regression models can b…
Subspace Clustering of Very Sparse High-Dimensional Data
Hankui Peng, Nicos Pavlidis, Idris Eckley +1
In this paper we consider the problem of clustering collections of very short texts using subspace clustering. This problem arises in many applications such as product categorisati…
Minimum Spectral Connectivity Projection Pursuit
David P. Hofmeyr, Nicos G. Pavlidis, Idris A. Eckley
We study the problem of determining the optimal low dimensional projection for maximising the separability of a binary partition of an unlabelled dataset, as measured by spectral g…
Minimum Density Hyperplanes
Nicos G. Pavlidis, David P. Hofmeyr, Sotiris K. Tasoulis
Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning a…