This paper investigates graph clustering in the planted cluster model in the presence of small clusters. Traditional results dictate that for an algorithm to provably correctly recover the clusters, all clusters must be sufficiently large (in particular, Ω̃( √ n) where n is the number of nodes of the graph). We show that this is not really a restriction: by a more refined analysis of the trace-n...

Coughs sounds have shown promising as a potential marker for distinguishing COVID individuals from non-COVID ones. In this paper, we propose an attention-based ensemble learning approach to learn complementary representations cough samples. Unlike most traditional schemes such mere maxing or averaging, the proposed fairly considers contribution of representation generated by each single model. ...

We demonstrate that spiking neural networks encoding information in the timing of single spikes are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on real-world data, and we demonstrate how temporal synchrony in a multi-layer network can induce h...

Swarm Intelligence (SI) is an innovative artificial intelligence technique for solving complex optimization problems. Data clustering is the process of grouping data into a number of clusters. The goal of data clustering is to make the data in the same cluster share a high degree of similarity while being very dissimilar to data from other clusters. Clustering algorithms have been applied to a ...

Clustering aims at finding hidden structure in data. In this paper we present a new clustering algorithm that builds upon the local and global consistency method (Zhou, et.al., 2003), a semi-supervised learning technique with the property of learning very smooth functions with respect to the intrinsic structure revealed by the data. Starting from this algorithm, we derive an optimization framew...

In unsupervised domain adaptation (UDA), a classifier for the target is trained with massive true-label data from source and unlabeled domain. However, collecting in can be expensive sometimes impractical. Compared to true label (TL), complementary (CL) specifies class that pattern does not belong to, hence, CLs would less laborious than TLs. this article, we propose novel setting where compose...

This paper presents a new multitask learning framework that learns a shared representation among the tasks, incorporating both task and feature clusters. The jointlyinduced clusters yield a shared latent subspace where task relationships are learned more effectively and more generally than in state-of-the-art multitask learning methods. The proposed general framework enables the derivation of m...

We present a learning and memory architecture that allows a robot companion to incrementally learn and associate data from different sensors and actuators. We use a topology learning algorithm that clusters the received inputs into discrete categories. On top of these clusters we apply associative learning methods to store co-occurrence relationships in an associative network. We evaluated the ...