Information discovery plays a vital role in the success of various machine learning and data-
driven tasks. In essence, it is a process of exploring useful knowledge from input data …

A fundamental task in data exploration is to extract simplified low dimensional
representations that capture intrinsic geometry in data, especially for faithfully visualizing …

It is a challenge for manifold discovery from the data in the high-dimensional space, since
the data in the high-dimensional space is sparsely distributed, which hardly provides rich …

This thesis proposes a manifold learning framework for visual data analysis from four
aspects: 1) extracting manifold structure of the visual data sets; 2) preserving natural tensor …

Manifold is considered to be the explicit form of data, so the smoothness of manifold is
related to data dimensionality. Data becomes sparse in the high-dimensional space, which …

Deep model is a useful tool that can extract smooth manifold from data. As a computationally
intensive method, however, model parameters and data sparsity are common factors that …

In this paper, we propose a novel filter based unsupervised feature selection algorithm. We
first extract the global level manifold structure using LLE on all features. We also extract the …

Dimensionality reduction has been playing a significant role in many fields such as
recognition, classification, clustering, high-dimensionality data compression. However, due …

Different local manifold learning methods are developed based on different geometric
intuitions and each method only learns partial information of the true geometric structure of …

Performing effective image retrieval tasks, capable of exploiting the underlying structure of
datasets still constitutes a challenge research scenario. This paper proposes a novel …