The Internet-of-Things (IoT) technology is widely used in various fields. In the Earth
observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and …

This paper presents DeepSecure, the an scalable and provably secure Deep Learning (DL)
framework that is built upon automated design, efficient logic synthesis, and optimization …

Subspace clustering methods based on ell_1, l_2 or nuclear norm regularization have
become very popular due to their simplicity, theoretical guarantees and empirical success …

Subspace clustering refers to the problem of segmenting data drawn from a union of
subspaces. State-of-the-art approaches for solving this problem follow a two-stage …

State-of-the-art subspace clustering methods are based on the self-expressive model, which
represents each data point as a linear combination of other data points. However, such …

State-of-the-art subspace clustering methods are based on expressing each data point as a
linear combination of other data points while regularizing the matrix of coefficients with l_1 …

Abstract Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR) are both
considered as the state-of-the-art methods for {\em subspace clustering}. The two methods …

This paper considers the problem of subspace clustering under noise. Specifically, we study
the behavior of Sparse Subspace Clustering (SSC) when either adversarial or random noise …

State-of-the-art subspace clustering methods are based on self-expressive model, which
represents each data point as a linear combination of other data points. By enforcing such …

Many computer vision tasks involve processing large amounts of data contaminated by
outliers, which need to be detected and rejected. While outlier detection methods based on …