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Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).
We propose FineGAN, a novel unsupervised GAN framework, which disentangles the background, object shape, and object appearance to hierarchically generate images of fine-grained object categories.
In this paper, we propose and study an algorithm, called Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of low-dimensional subspaces.
#4 best model for Image Clustering on Extended Yale-B
We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is best able to find the most clusterable manifold in the embedding, suggesting local manifold learning on an autoencoded embedding is effective for discovering higher quality discovering clusters.
SOTA for Image Clustering on Fashion-MNIST
Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabelled data.
In addition to various spatial fusion-based methods, an affinity fusion-based network is also proposed in which the self-expressive layer corresponding to different modalities is enforced to be the same.
SOTA for Image Clustering on Extended Yale-B
Typically, they use multinomial logistic regression posteriors and parameter regularization, as is very common in supervised learning.