Some methods for classi cation and analysis of multivariate observations

By J. Mcqueen. 1967

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Citations in the corpus

Exploring Simple Siamese Representation Learning (2020, methodology): “This formulation is analogous to k-means clustering [28].”
SoundStream: An End-to-End Neural Audio Codec (2021, methodology): “Optimal scalar quantization based on Lloyd’s algorithm [25] can be extended to a high-dimensional space via the generalized Lloyd algorithm (GLA) [26], which is very similar to k-means clustering [27].”
Learning to cluster in order to Transfer across domains and tasks (2017, methodology): “Since the number of pairs is high and it is not feasible to feed them into other constrained clustering algorithms, we compare CCN with K-means, LSC(Chen & Cai, 2011), and LPNMF (Cai et al., 2009).”
Continual Learning for Text Classification with Information Disentanglement Based Regularization (2021, methodology): “…catastrophic forgetting without much increases of memory and training time, we propose to augment our regularization-based methods by storing and replaying only a small amount of representative examples (e.g., 1% samples selected by memory selection rules like K-Means (MacQueen et al., 1967)).”
AdaSpeech 4: Adaptive Text to Speech in Zero-Shot Scenarios (2022, background): “…ability on new speakers in three steps: ensure the basic vectors to be dissimilar (ideally need to be orthogonal), we initialize basis vectors with the cluster centers (by k -means [21]) of pre-trained speaker embeddings and encourage the basis vectors to be dissimilar with a regularization loss.”
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks (2016, methodology): “For (1), we ﬁrst embed all of the images in a class in a two-dimensional space via PCA (Person, 1901; Jolliffe, 2002) and t-SNE (Van der Maaten et al., 2008), and then perform k -means clustering (MacQueen et al., 1967) to ﬁnd k types of images (Fig.”
Unsupervised Deep Embedding for Clustering Analysis (2015, methodology): “One branch of popular methods for clustering is k - means (MacQueen et al., 1967) and Gaussian Mixture Models (GMM) (Bishop, 2006).”
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics (2017, background): “Crucially, it does not assume knowledge of the number of clusters like algorithms such as k-means [34].”
Joint Unsupervised Learning of Deep Representations and Image Clusters (2016, methodology): “We compare our approach with 12 clustering algorithms, including K-means [39], NJW spectral clustering (SC-NJW) [43], self-tuning spectral clustering (SC-ST)[67], large-scale spectral clustering (SC-LS) [3], agglomerative clustering with average linkage (AC-Link)[25], Zeta function based…”
Archetypical motion: Supervised game behavior learning with Archetypal Analysis (2013, methodology): “The latter strategy has been proposed by Thurau et al. [17] and we follow their suggestion in that we we cluster our training set of player actions using k -means clustering [21] in order to determine action primitives.”
Surpassing the Limit: Keyword Clustering to Improve Twitter Sample Coverage (2015, methodology): “Rather than using spectral clustering, however, we use K -Means clustering [15].”
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression (2007, methodology): “Representative methods include the K-means algorithm [6]–[9] (or its variants [10]–[12]) and the Expectation Maximization (EM) algorithm [13], [14], which is essentially a greedy descent algorithm to ﬁnd the maximum-likelihood (ML) estimate of a mixture of probabilistic (Gaussian) distributions…”
Model-Based Clustering, Discriminant Analysis, and Density Estimation (2002, methodology): “KEY WORDS: Bayes factor; Breast cancer diagnosis; Cluster analysis; EM algorithm; Gene expression microarray data; Markov chain Monte Carlo; Mixture model; Outliers; Spatial point process.”
Active localization with dynamic obstacles (2016, methodology): “The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm [23] was selected among the several clustering algorithms, mainly because, unlike other classical clustering algorithms (e.g., k -means [24]), it does not require the number of clusters to be given as input.”