LEADER 01193nam a2200277 i 4500 001 991001237729707536 008 051003s2001 ne ab b 001 0 eng d 020 $a9057820900 035 $ab13341340-39ule_inst 040 $aDi.S.Te.B.A.$beng 082 00$a595.32$222 100 1 $aOrlova-Bienkowskaja, Marina Y.$0622748 245 10$aCladocera, Anomopoda :$bDaphniidae, genus Simocephalus /$cby Marina Y. Orlova-Bienkowskaja 246 18$aDaphniidae, genus Simocephalus 260 $aLeiden :$bBackhuys,$c2001 300 $avii, 130 p. :$bill., maps ;$c24 cm 440 0$aGuides to the identification of the microinvertebrates of the continental waters of the world,$x0928-2440 ;$v17 504 $aIncludes bibliographical references (p. 121-125) and index 650 0$aTailless water fleas$vIdentification 650 0$aTailless water fleas$vPictorial works 907 $a.b13341340$b21-09-06$c03-10-05 912 $a991001237729707536 945 $aLE003 595 GUI01.01 V.17 (2001)$g1$i2003000093957$lle003$op$pE43.00$q-$rl$s- $t0$u4$v19$w4$x0$y.i14188016$z02-02-06 996 $aCladocera, Anomopoda$91092365 997 $aUNISALENTO 998 $ale003$b03-10-05$cm$da $e-$feng$gne $h0$i0 LEADER 03671oam 2200481 450 001 9910299049703321 005 20190911112726.0 010 $a3-319-03943-1 024 7 $a10.1007/978-3-319-03943-5 035 $a(OCoLC)869462800 035 $a(MiFhGG)GVRL6WZI 035 $a(EXLCZ)993710000000083674 100 $a20131202d2014 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aOpen problems in spectral dimensionality reduction /$fHarry Strange, Reyer Zwiggelaar 205 $a1st ed. 2014. 210 1$aCham [Switzerland] :$cSpringer,$d2014. 215 $a1 online resource (ix, 92 pages) $cillustrations (some color) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 300 $a"ISSN: 2191-5768." 311 $a3-319-03942-3 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Spectral Dimensionality Reduction -- Modelling the Manifold -- Intrinsic Dimensionality -- Incorporating New Points -- Large Scale Data -- Postcript. 330 $aThe last few years have seen a great increase in the amount of data available to scientists. Datasets with millions of objects and hundreds, if not thousands of measurements are now commonplace in many disciplines. However, many of the computational techniques used to analyse this data cannot cope with such large datasets. Therefore, strategies need to be employed as a pre-processing step to reduce the number of objects, or measurements, whilst retaining important information inherent to the data. Spectral dimensionality reduction is one such family of methods that has proven to be an indispensable tool in the data processing pipeline. In recent years the area has gained much attention thanks to the development of nonlinear spectral dimensionality reduction methods, often referred to as manifold learning algorithms. Numerous algorithms and improvements have been proposed for the purpose of performing spectral dimensionality reduction, yet there is still no gold standard technique. Those wishing to use spectral dimensionality reduction without prior knowledge of the field will immediately be confronted with questions that need answering: What parameter values to use? How many dimensions should the data be embedded into? How are new data points incorporated? What about large-scale data? For many, a search of the literature to find answers to these questions is impractical, as such, there is a need for a concise discussion into the problems themselves, how they affect spectral dimensionality reduction, and how these problems can be overcome. This book provides a survey and reference aimed at advanced undergraduate and postgraduate students as well as researchers, scientists, and engineers in a wide range of disciplines. Dimensionality reduction has proven useful in a wide range of problem domains and so this book will be applicable to anyone with a solid grounding in statistics and computer science seeking to apply spectral dimensionality to their work. 410 0$aSpringerBriefs in computer science. 606 $aDimension reduction (Statistics) 606 $aComputer science$xMathematics 615 0$aDimension reduction (Statistics) 615 0$aComputer science$xMathematics. 676 $a005.7 676 $a006.3 700 $aStrange$b Harry$4aut$4http://id.loc.gov/vocabulary/relators/aut$0918902 702 $aZwiggelaar$b Reyer 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910299049703321 996 $aOpen Problems in Spectral Dimensionality Reduction$92060887 997 $aUNINA