Collective Matrix Factorization of Predictors, Neighborhood and Targets for Semi-supervised Classification

2014 ◽  
pp. 286-297 ◽  
Author(s):  
Lucas Rego Drumond ◽  
Lars Schmidt-Thieme ◽  
Christoph Freudenthaler ◽  
Artus Krohn-Grimberghe
Keyword(s):  
Matrix Factorization ◽  
Supervised Classification

scholarly journals Deep Correlated Predictive Subspace Learning for Incomplete Multi-View Semi-Supervised Classification

2019 ◽  
Author(s):  
Zhe Xue ◽  
Junping Du ◽  
Dawei Du ◽  
Wenqi Ren ◽  
Siwei Lyu
Keyword(s):  
Matrix Factorization ◽  
Supervised Classification ◽  
State Of The Art ◽  
Subspace Learning ◽  
Experimental Results ◽  
Private Label ◽  
Unified Framework ◽  
Class Label ◽  
Proper Subspace ◽  
Label Prediction

Incomplete view information often results in failure cases of the conventional multi-view methods. To address this problem, we propose a Deep Correlated Predictive Subspace Learning (DCPSL) method for incomplete multi-view semi-supervised classification. Specifically, we integrate semi-supervised deep matrix factorization, correlated subspace learning, and multi-view label prediction into a unified framework to jointly learn the deep correlated predictive subspace and multi-view shared and private label predictors. DCPSL is able to learn proper subspace representation that is suitable for class label prediction, which can further improve the performance of classification. Extensive experimental results on various practical datasets demonstrate that the proposed method performs favorably against the state-of-the-art methods.

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

PEMANFAATAN DATA PENGINDERAAN JAUH DAN TEKNOLOGI SIG UNTUK MENGIDENTIFIKASI BUILT UP AREA DAN KAITANNYA DENGAN RTRW DI KABUPATEN KENDAL

2019 ◽  
Vol 3 ◽  
pp. 521
Author(s):  
Mailendra Mailendra
Keyword(s):  
Supervised Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm

Integrasi data penginderaan jauh dengan sistem informasi geografis telah banyak dikembangkan, dan salah satunya dalam melihat perkembangan lahan terbangun. Tujuan penelitian ini adalah untuk melihat perkembangan lahan terbangun dan kesesuaiannya dengan Rencana Pola Ruang Kabupaten Kendal. Kemudian metode yang digunakan yaitu metode supervised classification dengan memanfaatkan data citra landsat 5 TM dan landsat 8 OLI yang selanjutnya dihitung luas dari masing lahan terbangun berdasarkan data temporal tahun 1990, tahun 2015 dan tahun 2017. Setelah diketahui luas lahan terbangun selanjutnya dioverlay dengan peta rencana pola ruang Kabupaten Kendal untuk melihat sesuai atau tidaknya penempatan lahan terbangun tersebut. Adapun hasil penelitiannya yaitu setiap tahunnya lahan terbangun terus meningkat di Kabupaten Kendal, terjadi peningkatan yang cukup signifikan dalam dua tahun terakhir yaitu tahun 2015 hingga tahun 2017. Selanjutnya diperkirakan 88 % lahan terbangun tersebut telah sesuai dengan RTRW karena sudah berada pada kawasan budidaya.

PEMETAAN HABITAT BENTIK INTERTIDAL: Informasi Awal Perencanaan Pengelolaan Pesisir Laut Secara Spasial di Bulak Setra dan Batu Karas, Kabupaten Pangandaran

2018 ◽  
Vol 2 ◽  
pp. 105
Author(s):  
Rendra Pranata
Keyword(s):  
Dissolved Oxygen ◽  
Supervised Classification ◽  
Landsat 7 ◽  
Avicennia Alba

<p>Ekosistem pesisir Kabupaten Pangandaran memiliki biodiversitas yang cukup tinggi, namun pasca-tsunami tahun 2006 terjadi penurunan kerapatan ekosistem mangrove akibat rusaknya daerah pesisir dan wilayah permukiman sepanjang 28 km. Penelitian ini bertujuan untuk mengetahui kondisi habitat bentik di kawasan intertidal seperti mangrove dan makrozoobentos, serta mengukur parameter kualitas air. Metode yang digunakan yaitu interpretasi citra Landsat 7 tahun 2017 dengan melakukan <em>masking</em> dan <em>supervised classification</em> untuk mengetahui daerah tutupan mangrove di Bulak Setra dan Batu Karas, kemudian dilakukan identifikasi mangrove dengan transek kuadran 10x10 meter sepanjang 50 meter ke arah laut pada 7 plot di Bulak Setra dan 14 plot di Batu Karas untuk validasi data citra satelit. Selain itu juga dilakukan pengukuran parameter kualitas air serta identifikasi makrozoobentos. Hasil penelitian menunjukkan bahwa mangrove di Bulak Setra didominasi oleh <em>Scyphiphora hydrophyllacea</em> dari 8 spesies lain yang ditemukan dengan Indeks Nilai Penting (INP) 94,41%, sedangkan di Batu Karas didominasi oleh <em>Avicennia alba</em> dari 8 spesies lain yang ditemukan dengan INP 157%. Nilai rata-rata parameter kualitas air di Bulak Setra dan Batu Karas berturut-turut yaitu suhu 30<sup>o</sup>C dan 29,41<sup>o</sup>C, salinitas 5,56 psu dan 27,23 psu, pH 7,48 dan 6,86 serta konsentrasi <em>Dissolved Oxygen</em> (DO) 5,2 dan 6,5 mg/L. Makrozoobentos didominasi oleh kelas <em>G</em><em>astropoda</em>. Faktor sosial ekonomi masyarakat juga disajikan sebagai informasi sumber daya manusia yang akan berperan menjadi komponen pembangunan pengelolaan pesisir. Diharapkan penelitian ini dapat menjadi informasi awal dalam pengelolaan perencanaan wilayah pesisir di Bulak Setra dan Batu Karas.<strong></strong></p><p><strong>Kata kunci</strong>: bentik, intertidal, mangrove</p>

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