Probabilistic relational PCA (PRPCA) can learn a projection matrix to perform dimensionality reduction for relational data. However, the results learned by PRPCA lack interpretabi...
Abstract. In many applications of Augmented Reality (AR), rectangular markers are tracked in real time by capturing with cameras. In this paper, we consider the AR application in w...
In this paper we introduce a new embedding technique to find the linear projection that best projects labeled data samples into a new space where the performance of a Nearest Neig...
Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in...
Compressed sensing (CS), a joint compression and sensing process, is a emerging field of activity in which the signal is sampled and simultaneously compressed at a greatly reduced...
Vo Dinh Minh Nhat, Duc Vo, Subhash Challa, Sungyou...
This paper presents a forgetting factor scheme for variable step-size affine projection algorithms (APA). The proposed scheme uses a forgetting processed input matrix as the projec...
In this work we present an efficient coding scheme suitable for lossy image compression using a lattice vector quantizer (LVQ) based on statistically independent data projections...
Leonardo H. Fonteles, Marc Antonini, Ronald Phlypo
This article introduces the absolute quadratic complex formed by all lines that intersect the absolute conic. If denotes the 3 ? 3 symmetric matrix representing the image of that ...