Unsupervised Approach for Structure Preserving Dimensionality Reduction

2006 ◽  
Author(s):  
Amit Saxena ◽  
Megha Kothari
Keyword(s):  
Dimensionality Reduction ◽  
Structure Preserving ◽  
Unsupervised Approach

Structure preserving dimensionality reduction for visual object recognition

2018 ◽  
Vol 77 (18) ◽  
pp. 23529-23545 ◽  
Author(s):  
Jinjoo Song ◽  
Gangjoon Yoon ◽  
Heeryon Cho ◽  
Sang Min Yoon
Keyword(s):  
Object Recognition ◽  
Dimensionality Reduction ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Structure Preserving

scholarly journals Single-Cell Manifold Preserving Feature Selection (SCMER)

2020 ◽  
Author(s):  
Ken Chen ◽  
Shaoheng Liang ◽  
Vakul Mohanty ◽  
Jinzhuang Dou ◽  
Miao Qi ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cost Effective ◽  
High Dimensional ◽  
Compact Set ◽  
Cell Lineages ◽  
Molecular Features ◽  
Unsupervised Approach ◽  
Cell Data ◽  
Common Cell

Abstract A key challenge in studying organisms and diseases is to detect rare molecular programs and rare cell populations (RCPs) that drive development, differentiation, and transformation. Molecular features such as genes and proteins defining RCPs are often unknown and difficult to detect from unenriched single-cell data, using conventional dimensionality reduction and clustering-based approaches. Here, we propose a novel unsupervised approach, named SCMER, which performs UMAP style dimensionality reduction via selecting a compact set of molecular features with definitive meanings. We applied SCMER in the context of hematopoiesis, lymphogenesis, tumorigenesis, and drug resistance and response. We found that SCMER can identify non-redundant features that sensitively delineate both common cell lineages and rare cellular states ignored by current approaches. SCMER can be widely used for discovering novel molecular features in a high dimensional dataset, designing targeted, cost-effective assays for clinical applications, and facilitating multi-modality integration.

scholarly journals scSemiAE: A Deep Model with Semi-Supervised Learning for Single-Cell Transcriptomics

2021 ◽  
Author(s):  
Jiayi Dong ◽  
Yin Zhang ◽  
Fei Wang
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cell Subpopulation ◽  
High Dimensions ◽  
Deep Model ◽  
Unsupervised Approach ◽  
Cell Subpopulations ◽  
Downstream Analysis ◽  
Low Dimensional ◽  
Public Datasets

Abstract Background: With the development of modern sequencing technology, hundreds of thousands of single-cell RNA-sequencing(scRNA-seq) profiles allow to explore the heterogeneity in the cell level, but it faces the challenges of high dimensions and high sparsity. Dimensionality reduction is essential for downstream analysis, such as clustering to identify cell subpopulations. Usually, dimensionality reduction follows unsupervised approach. Results: In this paper, we introduce a semi-supervised dimensionality reduction method named scSemiAE, which is based on an autoencoder model. It transfers the information contained in available datasets with cell subpopulation labels to guide the search of better low-dimensional representations, which can ease further analysis. Conclusions: Experiments on five public datasets show that, scSemiAE outperforms both unsupervised and semi-supervised baselines whether the transferred information embodied in the number of labeled cells and labeled cell subpopulations is much or less.

scholarly journals Spectral-Locational-Spatial Manifold Learning for Hyperspectral Images Dimensionality Reduction

2021 ◽  
Vol 13 (14) ◽  
pp. 2752
Author(s):  
Na Li ◽  
Deyun Zhou ◽  
Jiao Shi ◽  
Tao Wu ◽  
Maoguo Gong
Keyword(s):  
Dimensionality Reduction ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Cluster Centroid ◽  
Intrinsic Structure ◽  
Adjacency Graph ◽  
Structure Preserving ◽  
Class Labels

Dimensionality reduction (DR) plays an important role in hyperspectral image (HSI) classification. Unsupervised DR (uDR) is more practical due to the difficulty of obtaining class labels and their scarcity for HSIs. However, many existing uDR algorithms lack the comprehensive exploration of spectral-locational-spatial (SLS) information, which is of great significance for uDR in view of the complex intrinsic structure in HSIs. To address this issue, two uDR methods called SLS structure preserving projection (SLSSPP) and SLS reconstruction preserving embedding (SLSRPE) are proposed. Firstly, to facilitate the extraction of SLS information, a weighted spectral-locational (wSL) datum is generated to break the locality of spatial information extraction. Then, a new SLS distance (SLSD) excavating the SLS relationships among samples is designed to select effective SLS neighbors. In SLSSPP, a new uDR model that includes a SLS adjacency graph based on SLSD and a cluster centroid adjacency graph based on wSL data is proposed, which compresses intraclass samples and approximately separates interclass samples in an unsupervised manner. Meanwhile, in SLSRPE, for preserving the SLS relationship among target pixels and their nearest neighbors, a new SLS reconstruction weight was defined to obtain the more discriminative projection. Experimental results on the Indian Pines, Pavia University and Salinas datasets demonstrate that, through KNN and SVM classifiers with different classification conditions, the classification accuracies of SLSSPP and SLSRPE are approximately 4.88%, 4.15%, 2.51%, and 2.30%, 5.31%, 2.41% higher than that of the state-of-the-art DR algorithms.

scholarly journals Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Bingfeng Li ◽  
Yandong Tang ◽  
Zhi Han
Keyword(s):  
Dimensionality Reduction ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Noisy Data ◽  
Nonnegative Matrix ◽  
Structure Preserving ◽  
Structure Preservation ◽  
Significant Performance ◽  
Linear Dimensionality Reduction ◽  
Low Dimensional

As a linear dimensionality reduction method, nonnegative matrix factorization (NMF) has been widely used in many fields, such as machine learning and data mining. However, there are still two major drawbacks for NMF: (a) NMF can only perform semantic factorization in Euclidean space, and it fails to discover the intrinsic geometrical structure of high-dimensional data distribution. (b) NMF suffers from noisy data, which are commonly encountered in real-world applications. To address these issues, in this paper, we present a new robust structure preserving nonnegative matrix factorization (RSPNMF) framework. In RSPNMF, a local affinity graph and a distant repulsion graph are constructed to encode the geometrical information, and noisy data influence is alleviated by characterizing the data reconstruction term of NMF withl2,1-norm instead ofl2-norm. With incorporation of the local and distant structure preservation regularization term into the robust NMF framework, our algorithm can discover a low-dimensional embedding subspace with the nature of structure preservation. RSPNMF is formulated as an optimization problem and solved by an effective iterative multiplicative update algorithm. Experimental results on some facial image datasets clustering show significant performance improvement of RSPNMF in comparison with the state-of-the-art algorithms.

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