Semi-supervised k-means++

Yoder, Jordan; Priebe, Carey E.

doi:10.1080/00949655.2017.1327588

Cited by 42 publications

(23 citation statements)

References 9 publications

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“…We aim to demonstrate that the NUMA optimizations we deploy for knor are applicable to a variety of compute-intensive applications. The initial phase will target other variants of k-means like spherical k-means [17], semisupervised k-means++ [40] etc. Later phases will target machine learning algorithms like GMM [15], agglomerative clustering [34] and k-nearest neighbors [10].…”

Section: Future Work and Discussionmentioning

confidence: 99%

knor

Mhembere

Zheng

Priebe

et al. 2017

Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing

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k-means is one of the most influential and utilized machine learning algorithms. Its computation limits the performance and scalability of many statistical analysis and machine learning tasks. We rethink and optimize k-means in terms of modern NUMA architectures to develop a novel parallelization scheme that delays and minimizes synchronization barriers. The k-means NUMA Optimized Routine (knor) library has (i) in-memory (knori), (ii) distributed memory (knord), and (iii) semi-external memory (knors) modules that radically improve the performance of k-means for varying memory and hardware budgets. knori boosts performance for single machine datasets by an order of magnitude or more. knors improves the scalability of k-means on a memory budget using SSDs. knors scales to billions of points on a single machine, using a fraction of the resources that distributed in-memory systems require. knord retains knori's performance characteristics, while scaling in-memory through distributed computation in the cloud. knor modifies Elkan's triangle inequality pruning algorithm such that we utilize it on billion-point datasets without the significant memory overhead of the original algorithm. We demonstrate knor outperforms distributed commercial products like H 2 O, Turi (formerly Dato, GraphLab) and Spark's MLlib by more than an order of magnitude for datasets of 10 7 to 10 9 points.

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Section: Future Work and Discussionmentioning

confidence: 99%

knor

Mhembere

Zheng

Priebe

et al. 2017

Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing

Self Cite

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“…The erroneous GT maps with various patch sizes were used as input for GWENN-SS and ss-Kmeans++. 10 ss-Kmeans++ is a recent semi-supervised extension of Kmeans++ which can also deal with semi-supervised information, i.e. LS sets.…”

Section: Experiments 2: Salinas Hsimentioning

confidence: 99%

GWENN-SS: a simple semi-supervised nearest-neighbor density-based classification method with application to hyperspectral images

Cariou

Chehdi

Moan

2019

Image and Signal Processing for Remote Sensing XXV

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In this communication, we address the problem of semi-supervised classification under conditions where (i) learning samples are available only for specific classes and potentially mislabeled, and (ii) the actual number of classes is unknown. For this, we propose a semi-supervised extension of a Nearest-Neighbor-Density Based clustering method, namely the Graph WatershEd using Nearest Neighbor (GWENN) method. We show how an incomplete, erroneous learning sample (LS) set can be incorporated in the algorithm in order to produce efficient labeling decisions partly guided by a priori information, and to discover new classes and correct mislabeled objects. The efficiency of the proposed method, named GWENN-SS, is demonstrated experimentally. We first evaluate its robustness with simulated data for which an erroneous and incomplete LS set is given. We then assess the reliability of GWENN-SS on real hyperspectral images and we show that it can outperform a recent similar semi-supervised approach.

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“…A DNN-enabled authenticator can be trained in a semi-supervised manner with both the labeled and unlabeled data. This can be done by assigning pseudo labels to the unlabeled samples, which are then exploited as if they are actually labeled [22]- [24]. The pre-trained network can be further fine-tuned by only using the labeled channel observations [25].…”

Section: B Our Workmentioning

confidence: 99%

Supervised and Semi-Supervised Deep Neural Networks for CSI-Based Authentication

Wang,

Li,

Chen

et al. 2018

Preprint

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From the viewpoint of physical-layer authentication, spoofing attacks can be foiled by checking channel state information (CSI). Existing CSI-based authentication algorithms mostly require a deep knowledge of the channel to deliver decent performance. In this paper, we investigate CSI-based authenticators that can spare the effort to predetermine channel properties by utilizing deep neural networks (DNNs). We first propose a convolutional neural network (CNN)-enabled authenticator that is able to extract the local features in CSI. Next, we employ the recurrent neural network (RNN) to capture the dependencies between different frequencies in CSI. In addition, we propose to use the convolutional recurrent neural network (CRNN)-a combination of the CNN and the RNN-to learn local and contextual information in CSI for user authentication. To effectively train these DNNs, one needs a large amount of labeled channel records. However, it is often expensive to label large channel observations in the presence of a spoofer. In view of this, we further study a case in which only a small part of the the channel observations are labeled. To handle it, we extend these DNNs-enabled approaches into semi-supervised ones. This extension is based on a semi-supervised learning technique that employs both the labeled and unlabeled data to train a DNN. To be specific, our semi-supervised method begins by generating pseudo labels for the unlabeled channel samples through implementing the K-means algorithm in a semi-supervised manner. Subsequently, both the labeled and pseudo labeled data are exploited to pre-train a DNN, which is then fine-tuned based on the labeled channel records. Finally, simulation and experimental results show that the proposed DNNs-enabled schemes can significantly outperform the benchmark designs, and their semi-supervised extensions can yield excellent performance even with limited labeled samples.

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Semi-supervised k-means++

Cited by 42 publications

References 9 publications

knor

knor

GWENN-SS: a simple semi-supervised nearest-neighbor density-based classification method with application to hyperspectral images

Supervised and Semi-Supervised Deep Neural Networks for CSI-Based Authentication

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