20 citations · 59 across the 22 of their papers we have counts for
11 papers · 1 filter
Improving the Predictive Performances of Nearest Neighbors Learning by Efficient Variable Selection
Eddie Pei, Ernest Fokoue
This paper computationally demonstrates a sharp improvement in predictive performance for nearest neighbors thanks to an efficient forward selection of the predictor variables.…
A Computational Exploration of Emerging Methods of Variable Importance Estimation
Louis Mozart Kamdem, Ernest Fokoue
Estimating the importance of variables is an essential task in modern machine learning. This help to evaluate the goodness of a feature in a given model. Several techniques for est…
Nonnegative Matrix Factorization with Zellner Penalty
Matthew Corsetti, Ernest Fokoué
Nonnegative matrix factorization (NMF) is a relatively new unsupervised learning algorithm that decomposes a nonnegative data matrix into a parts-based, lower dimensional, linear r…
Nonnegative Matrix Factorization with Toeplitz Penalty
Matthew Corsetti, Ernest Fokoué
Nonnegative Matrix Factorization (NMF) is an unsupervised learning algorithm that produces a linear, parts-based approximation of a data matrix. NMF constructs a nonnegative low ra…
Graph Enhanced High Dimensional Kernel Regression
E. Pei, E. Fokoué
In this paper, the flexibility, versatility and predictive power of kernel regression are combined with now lavishly available network data to create regression models with even gr…
On the Statistical Challenges of Echo State Networks and Some Potential Remedies
Qiuyi Wu, Ernest Fokoue, Dhireesha Kudithipudi
Echo state networks are powerful recurrent neural networks. However, they are often unstable and shaky, making the process of finding an good ESN for a specific dataset quite hard.…
A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler
James Mnatzaganian, Ernest Fokoué, Dhireesha Kudithipudi
Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The algorithm, in…
On the Predictive Properties of Binary Link Functions
Necla Gunduz, Ernest Fokoue
This paper provides a theoretical and computational justification of the long held claim that of the similarity of the probit and logit link functions often used in binary classifi…
Random Subspace Learning Approach to High-Dimensional Outliers Detection
Bohan Liu, Ernest Fokoue
We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and…
A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining
Ernest Fokoue
Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and socia…
Probit Normal Correlated Topic Models
Xingchen Yu, Ernest Fokoue
The logistic normal distribution has recently been adapted via the transformation of multivariate Gaus- sian variables to model the topical distribution of documents in the presenc…