Author name cluster

Keith Chan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2011 Conference Paper

Ordinal Regression via Manifold Learning

Yang Liu
Yan Liu
Keith Chan

Ordinal regression is an important research topic in machine learning. It aims to automatically determine the implied rating of a data item on a ﬁxed, discrete rating scale. In this paper, we present a novel ordinal regression approach via manifold learning, which is capable of uncovering the embedded nonlinear structure of the data set according to the observations in the highdimensional feature space. By optimizing the order information of the observations and preserving the intrinsic geometry of the data set simultaneously, the proposed algorithm provides the faithful ordinal regression to the new coming data points. To offer more general solution to the data with natural tensor structure, we further introduce the multilinear extension of the proposed algorithm, which can support the ordinal regression of high order data like images. Experiments on various data sets validate the effectiveness of the proposed algorithm as well as its extension.

PDF Details

AAAI Conference 2010 Conference Paper

Multilinear Maximum Distance Embedding Via L1-Norm Optimization

Yang Liu
Yan Liu
Keith Chan

Dimensionality reduction plays an important role in many machine learning and pattern recognition tasks. In this paper, we present a novel dimensionality reduction algorithm called multilinear maximum distance embedding (M2 DE), which includes three key components. To preserve the local geometry and discriminant information in the embedded space, M2 DE utilizes a new objective function, which aims to maximize the distances between some particular pairs of data points, such as the distances between nearby points and the distances between data points from different classes. To make the mapping of new data points straightforward, and more importantly, to keep the natural tensor structure of high-order data, M2 DE integrates multilinear techniques to learn the transformation matrices sequentially. To provide reasonable and stable embedding results, M2 DE employs the L1-norm, which is more robust to outliers, to measure the dissimilarity between data points. Experiments on various datasets demonstrate that M2 DE achieves good embedding results of high-order data for classification tasks.

PDF Details