Search Results for author: Alfred O. Hero

Found 35 papers, 7 papers with code

Fair Structure Learning in Heterogeneous Graphical Models

no code implementations9 Dec 2021 Davoud Ataee Tarzanagh, Laura Balzano, Alfred O. Hero

Certain demographics may be over-represented in some detected communities and under-represented in others.

Fairness Model Selection

Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via Generative Models

1 code implementation27 Aug 2021 Yasin Yilmaz, Mehmet Aktukmak, Alfred O. Hero

The proposed algorithm is presented in detail for the commonly encountered heterogeneous datasets with real-valued (Gaussian) and categorical (multinomial) features.

Anomaly Detection Imputation +1

OrthoReg: Robust Network Pruning Using Orthonormality Regularization

1 code implementation10 Sep 2020 Ekdeep Singh Lubana, Puja Trivedi, Conrad Hougen, Robert P. Dick, Alfred O. Hero

To address this issue, we propose OrthoReg, a principled regularization strategy that enforces orthonormality on a network's filters to reduce inter-filter correlation, thereby allowing reliable, efficient determination of group importance estimates, improved trainability of pruned networks, and efficient, simultaneous pruning of large groups of filters.

Network Pruning

Adaptive multi-channel event segmentation and feature extraction for monitoring health outcomes

no code implementations20 Aug 2020 Xichen She, Yaya Zhai, Ricardo Henao, Christopher W. Woods, Christopher Chiu, Geoffrey S. Ginsburg, Peter X. K. Song, Alfred O. Hero

$\textbf{Conclusion}$: The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring.

Event Segmentation Transfer Learning

Predicting solar flares with machine learning: investigating solar cycle dependence

1 code implementation1 Dec 2019 Xiantong Wang, Yang Chen, Gabor Toth, Ward B. Manchester, Tamas I. Gombosi, Alfred O. Hero, Zhenbang Jiao, Hu Sun, Meng Jin, Yang Liu

A deep learning network, Long-Short Term Memory (LSTM) network, is used in this work to predict whether the maximum flare class an active region (AR) will produce in the next 24 hours is class $\Gamma$.

Solar and Stellar Astrophysics

A Geometric Approach to Online Streaming Feature Selection

no code implementations2 Oct 2019 Salimeh Yasaei Sekeh, Madan Ravi Ganesh, Shurjo Banerjee, Jason J. Corso, Alfred O. Hero

In this work, firstly, we assert that OSFS's main assumption of having data from all the samples available at runtime is unrealistic and introduce a new setting where features and samples are streamed concurrently called OSFS with Streaming Samples (OSFS-SS).

Testing that a Local Optimum of the Likelihood is Globally Optimum using Reparameterized Embeddings

no code implementations31 May 2019 Joel W. LeBlanc, Brian J. Thelen, Alfred O. Hero

When the problem is formulated in terms of maximizing the likelihood function under a statistical model for the measurements, one can construct a statistical test that a local maximum is in fact the global maximum.

Geometric Estimation of Multivariate Dependency

no code implementations21 May 2019 Salimeh Yasaei Sekeh, Alfred O. Hero

This paper proposes a geometric estimator of dependency between a pair of multivariate samples.

Density Estimation

Feature Selection for multi-labeled variables via Dependency Maximization

no code implementations10 Feb 2019 Salimeh Yasaei Sekeh, Alfred O. Hero

Feature selection and reducing the dimensionality of data is an essential step in data analysis.

General Classification

Learning to Bound the Multi-class Bayes Error

no code implementations15 Nov 2018 Salimeh Yasaei Sekeh, Brandon Oselio, Alfred O. Hero

Providing a tight bound on the BER that is also feasible to estimate has been a challenge.


Convergence Rates for Empirical Estimation of Binary Classification Bounds

no code implementations1 Oct 2018 Salimeh Yasaei Sekeh, Morteza Noshad, Kevin R. Moon, Alfred O. Hero

We derive a bound on the convergence rate for the Friedman-Rafsky (FR) estimator of the HP-divergence, which is related to a multivariate runs statistic for testing between two distributions.

Classification General Classification

Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment

1 code implementation1 Oct 2018 Inyong Yun, Cheolkon Jung, Xinran Wang, Alfred O. Hero, Joongkyu Kim

Pedestrians in videos have a wide range of appearances such as body poses, occlusions, and complex backgrounds, and there exists the proposal shift problem in pedestrian detection that causes the loss of body parts such as head and legs.

Pedestrian Detection

Parity Queries for Binary Classification

no code implementations4 Sep 2018 Hye Won Chung, Ji Oon Lee, Do-Yeon Kim, Alfred O. Hero

We define the query difficulty $\bar{d}$ as the average size of the query subsets and the sample complexity $n$ as the minimum number of measurements required to attain a given recovery accuracy.

Classification General Classification

Sequential Maximum Margin Classifiers for Partially Labeled Data

no code implementations7 Mar 2018 Elizabeth Hou, Alfred O. Hero

In many real-world applications, data is not collected as one batch, but sequentially over time, and often it is not possible or desirable to wait until the data is completely gathered before analyzing it.

A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks

no code implementations18 Dec 2017 Farshad Harirchi, Doohyun Kim, Omar A. Khalil, Sijia Liu, Paolo Elvati, Angela Violi, Alfred O. Hero

In this paper, we introduce a novel approach for the identification of the influential reactions in chemical reaction networks for combustion applications, using a data-driven sparse-learning technique.

Sparse Learning

A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks

no code implementations12 Dec 2017 Farshad Harirchi, Omar A. Khalil, Sijia Liu, Paolo Elvati, Angela Violi, Alfred O. Hero

In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network.

Sparse Learning

Fast Meta-Learning for Adaptive Hierarchical Classifier Design

1 code implementation9 Nov 2017 Gerrit J. J. van den Burg, Alfred O. Hero

The proposed empirical estimates of the Bayes error rate are computed from the minimal spanning tree (MST) of the samples from each pair of classes.

Classification General Classification +1

Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications

no code implementations21 Oct 2017 Sijia Liu, Jie Chen, Pin-Yu Chen, Alfred O. Hero

In this paper, we design and analyze a new zeroth-order online algorithm, namely, the zeroth-order online alternating direction method of multipliers (ZOO-ADMM), which enjoys dual advantages of being gradient-free operation and employing the ADMM to accommodate complex structured regularizers.

Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms

no code implementations8 Aug 2017 Pin-Yu Chen, Alfred O. Hero

Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks.

Graph Clustering Spectral Graph Clustering

Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity

no code implementations18 Apr 2017 Sijia Liu, Pin-Yu Chen, Alfred O. Hero

Our analysis reveals the connection between network topology design and the convergence rate of DDA, and provides quantitative evaluation of DDA acceleration for distributed optimization that is absent in the existing analysis.

Distributed Optimization Scheduling

Direct estimation of density functionals using a polynomial basis

no code implementations21 Feb 2017 Alan Wisler, Visar Berisha, Andreas Spanias, Alfred O. Hero

Typically, estimating these quantities requires complete knowledge of the underlying distribution followed by multi-dimensional integration.

Density Estimation

Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies

no code implementations16 Feb 2017 Elizabeth Hou, Kumar Sricharan, Alfred O. Hero

Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have real-world significance or not.

Unsupervised Anomaly Detection

Robust training on approximated minimal-entropy set

no code implementations21 Oct 2016 Tianpei Xie, Nasser. M. Narabadi, Alfred O. Hero

In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set.

Anomaly Detection General Classification +1

Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering

1 code implementation11 Apr 2016 Pin-Yu Chen, Alfred O. Hero

One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters.

Graph Clustering Model Selection +1

Incremental Method for Spectral Clustering of Increasing Orders

no code implementations23 Dec 2015 Pin-Yu Chen, Baichuan Zhang, Mohammad Al Hasan, Alfred O. Hero

The smallest eigenvalues and the associated eigenvectors (i. e., eigenpairs) of a graph Laplacian matrix have been widely used for spectral clustering and community detection.

Community Detection

Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection

no code implementations23 Dec 2015 Pin-Yu Chen, Sutanay Choudhury, Alfred O. Hero

Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful.

Dictionary Learning Intrusion Detection

Learning to classify with possible sensor failures

no code implementations16 Jul 2015 Tianpei Xie, Nasser M. Nasrabadi, Alfred O. Hero

In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set.

Anomaly Detection General Classification +1

Foundational principles for large scale inference: Illustrations through correlation mining

no code implementations11 May 2015 Alfred O. Hero, Bala Rajaratnam

Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed.

A Dictionary Approach to EBSD Indexing

no code implementations26 Feb 2015 Yu-Hui Chen, Se Un Park, Dennis Wei, Gregory Newstadt, Michael Jackson, Jeff P. Simmons, Marc De Graef, Alfred O. Hero

We discretize the domain of the forward model onto a dense grid of Euler angles and for each measured pattern we identify the most similar patterns in the dictionary.

Anomaly Detection

Empirical non-parametric estimation of the Fisher Information

1 code implementation6 Aug 2014 Visar Berisha, Alfred O. Hero

Traditional approaches to estimating the FIM require estimating the probability distribution function (PDF), or its parameters, along with its gradient or Hessian.

Density Estimation

Nonlinear unmixing of hyperspectral images: models and algorithms

no code implementations6 Apr 2013 Nicolas Dobigeon, Jean-Yves Tourneret, Cédric Richard, José C. M. Bermudez, Stephen McLaughlin, Alfred O. Hero

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM).

Multi-criteria Anomaly Detection using Pareto Depth Analysis

no code implementations NeurIPS 2012 Ko-Jen Hsiao, Kevin Xu, Jeff Calder, Alfred O. Hero

In such a case, multiple criteria can be defined, and one can test for anomalies by scalarizing the multiple criteria by taking some linear combination of them.

Anomaly Detection

Ensemble weighted kernel estimators for multivariate entropy estimation

no code implementations NeurIPS 2012 Kumar Sricharan, Alfred O. Hero

In this paper, it is shown that for sufficiently smooth densities, an ensemble of kernel plug-in estimators can be combined via a weighted convex combination, such that the resulting weighted estimator has a superior parametric MSE rate of convergence of order $O(T^{-1})$.

Efficient anomaly detection using bipartite k-NN graphs

no code implementations NeurIPS 2011 Kumar Sricharan, Alfred O. Hero

In this paper, we propose a novel bipartite k-nearest neighbor graph (BP-kNNG) anomaly detection scheme for estimating minimum volume sets.

Anomaly Detection

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