Multi-view clustering aims to partition multi-view data into different categories by optimally
exploring the consistency and complementary information from multiple sources. However …

In this article, we propose a novel feature selection approach, named unsupervised feature
selection with constrained-norm (row-sparsity constrained) and optimized graph (RSOGFS) …

As one of the most popular supervised dimensionality reduction methods, linear discriminant
analysis (LDA) has been widely studied in machine learning community and applied to …

Semi-supervised learning as one of most attractive problems in machine learning research
field has aroused broad attentions in recent years. In this paper, we propose a novel locality …

Unsupervised feature selection is an important topic in the fields of machine learning,
pattern recognition and data mining. The representation methods include adaptive-graph …

In this article, we propose a new unsupervised feature selection method named pseudo-
label guided structural discriminative subspace learning (PSDSL). Unlike the previous …

Linear discriminant analysis (LDA) uses labeled samples for acquiring a discriminant
projection direction, by which data of different categories are separated into distinct groups …

Clustering is a fashion method applied in machine learning tasks. However, high
dimensional data brings many obstacles for clustering approaches. To address such a …

Most of the existing graph-based clustering models performed clustering by adopting a two-
stage strategy which first completes the spectral embedding from a given fixed graph and …

Abstract The Iterative Closest Point (ICP) algorithm is one of the most important methods for
rigid registration between point sets. However, its performance begins to degenerate with …