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Deep clustering: A comprehensive survey
Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
Efficient deep embedded subspace clustering
Recently deep learning methods have shown significant progress in data clustering tasks.
Deep clustering methods (including distance-based methods and subspace-based …
Deep clustering methods (including distance-based methods and subspace-based …
Auto-weighted multi-view learning for image clustering and semi-supervised classification
Due to the efficiency of learning relationships and complex structures hidden in data, graph-
oriented methods have been widely investigated and achieve promising performance …
oriented methods have been widely investigated and achieve promising performance …
Automatic model construction with Gaussian processes
D Duvenaud - 2014 - repository.cam.ac.uk
This thesis develops a method for automatically constructing, visualizing and describing a
large class of models, useful for forecasting and finding structure in domains such as time …
large class of models, useful for forecasting and finding structure in domains such as time …
Rank-constrained spectral clustering with flexible embedding
Spectral clustering (SC) has been proven to be effective in various applications. However,
the learning scheme of SC is suboptimal in that it learns the cluster indicator from a fixed …
the learning scheme of SC is suboptimal in that it learns the cluster indicator from a fixed …
Beyond linear subspace clustering: A comparative study of nonlinear manifold clustering algorithms
Subspace clustering is an important unsupervised clustering approach. It is based on the
assumption that the high-dimensional data points are approximately distributed around …
assumption that the high-dimensional data points are approximately distributed around …
Deep clustering with sample-assignment invariance prior
Most popular clustering methods map raw image data into a projection space in which the
clustering assignment is obtained with the vanilla k-means approach. In this article, we …
clustering assignment is obtained with the vanilla k-means approach. In this article, we …
[HTML][HTML] Ensemble k-nearest neighbors based on centroid displacement
Abstract k-nearest neighbors (k-NN) is a well-known classification algorithm that is widely
used in different domains. Despite its simplicity, effectiveness and robustness, k-NN is …
used in different domains. Despite its simplicity, effectiveness and robustness, k-NN is …
See all by looking at a few: Sparse modeling for finding representative objects
We consider the problem of finding a few representatives for a dataset, ie, a subset of data
points that efficiently describes the entire dataset. We assume that each data point can be …
points that efficiently describes the entire dataset. We assume that each data point can be …
Structured sparse subspace clustering: A joint affinity learning and subspace clustering framework
Subspace clustering refers to the problem of segmenting data drawn from a union of
subspaces. State-of-the-art approaches for solving this problem follow a two-stage …
subspaces. State-of-the-art approaches for solving this problem follow a two-stage …