In this paper, we address two problems in Sparse Subspace Clustering algorithm (SSC), ie,
scalability issue and out-of-sample problem. SSC constructs a sparse similarity graph for …

We propose a new graph based hashing method called spectral embedded hashing (SEH)
for large-scale image retrieval. We first introduce a new regularizer into the objective …

Spectral clustering has aroused extensive attention in recent years. It performs well for the
data with arbitrary shape and can converge to global optimum. But traditional spectral …

Semi-supervised learning has become one of the hotspots in the field of machine learning in
recent years. It is successfully applied in clustering and improves the clustering …

A large number of data are generated in many real-world applications, eg, photos of albums
in social networks. Discovering meaningful patterns from them is desirable and still remains …

谱聚类算法中用亲和矩阵特征值最大的k 个特征向量并不总是能有效地发现数据集的结构.
为了选取较好特征向量, 提出了一种特征向量的Bagging 选取算法. 以成对约束计分方法为评价 …

Sparse subspace clustering (SSC) has achieved state‐of‐the‐art clustering quality by
performing spectral clustering over an ℓ1‐norm based similarity graph. However, SSC is a …

Recently, various clustering approaches were proposed that incorporated both local and
global information in an image dataset to learn nonlinear manifold. However, we have to …

谱聚类算法是近年来机器学习领域的研究热点, 它基于代数图论, 可以有效地解决很多实际问题.
但是传统的谱聚类算法无法很好地处理高维数据, 容易受到噪声和不相关属性的干扰 …

Manifold learning based image clustering models are usually employed at local level to deal
with images sampled from nonlinear manifold. Usually, gray level image features are used …