Multi-view learning aims to fully exploit the view-consistency and view-discrepancy for
performance improvement. Knowledge Distillation (KD), characterized by the so-called …

We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn
complex nonlinear transformations of multiple views (more than two) of data such that the …

Over the past few years, multimodal data analysis has emerged as an inevitable method for
identifying sample categories. In the multi-view data classification problem, it is expected …

In neuroscience, understanding inter-individual differences has recently emerged as a major
challenge, for which functional magnetic resonance imaging (fMRI) has proven invaluable …

In this article, we establish a family of subspace-based learning methods for multiview
learning using least squares as the fundamental basis. Specifically, we propose a novel …

One of the important issues associated with real-life high-dimensional data analysis is how
to extract significant and relevant features from multiview data. The multiset canonical …

The Canonical Correlation Analysis (CCA) family of methods is foundational in multi-view
learning. Regularised linear CCA methods can be seen to generalise Partial Least Squares …

Robust face clustering is a vital step in enabling computational understanding of visual
character portrayal in media. Face clustering for long-form content is challenging because of …

脑网络分析在研究大脑的认知活动, 探究大脑的信息处理模式和辅助精神类疾病的诊断等方面都
起着重要作用. 近年来, 基于多变量数据集的脑网络研究方法得到了普遍关注 …

This paper presents an important scene detection method based on anomaly detection
using a Long Short-Term Memory (LSTM) for baseball highlight generation. In order to deal …