K-Subspace clustering and its application in sparse component analysis

Image Classification via Sparse Representation and Subspace Alignment

Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...

Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut

Dimensionality reduction, cluster analysis, and sparse representation are among the cornerstones of machine learning. However, they seem unrelated to each other and are often applied independently in practice. In this paper, we discovered that the spectral graph theory underlies a series of these elementary methods and unifies them into a complete framework. The methods range from PCA, K-means,...

Estimating the mixing matrix in Sparse Component Analysis (SCA) based on partial k-dimensional subspace clustering

Learning Transformations for Clustering and Classification Learning Transformations for Clustering and Classification

A low-rank transformation learning framework for subspace clustering and classification is here proposed. Many high-dimensional data, such as face images and motion sequences, approximately lie in a union of low-dimensional subspaces. The corresponding subspace clustering problem has been extensively studied in the literature to partition such highdimensional data into clusters corresponding to...

Learning Robust Subspace Clustering

We propose a low-rank transformation-learning framework to robustify subspace clustering. Many high-dimensional data, such as face images and motion sequences, lie in a union of low-dimensional subspaces. The subspace clustering problem has been extensively studied in the literature to partition such highdimensional data into clusters corresponding to their underlying low-dimensional subspaces....